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REVIEW 4 major objections 5 minor 6 cited by

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This survey argues that LLM agents' growing autonomy turns ordinary model errors into persistent, irreversible security risks, and that safety must therefore be engineered as a formal constraint inside the agent's decision loop.

desk verdict A solid, well-organized survey of agent security whose R2A2 safety-guarantee claim does not survive scrutiny; the taxonomy is worth citing, but the formal math needs fixing. read the letter →

arxiv 2506.23844 v1 pith:B2PLYHCB submitted 2025-06-30 cs.AI

Hang Su , Jun Luo , Chang Liu , Xiao Yang , Yichi Zhang , Yinpeng Dong , Jun Zhu This is my paper
classification cs.AI
keywords LLMagentsautonomy-inducedriskAIagentsecurityConstrainedMarkovDecisionProcessmemorypoisoningtoolmisuseemergentmisalignmentreflectivesafetyarchitecture
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey tries to establish that the autonomy of LLM-based agents is not just an amplifier of existing AI risks but a transformation of them. The authors argue that once an agent has persistent memory, tool access, multi-step planning, self-reflection, and inter-agent communication, failures stop being confined to output text and become temporally deferred, cross-module, and sometimes irreversible. The paper maps this progression with a five-level autonomy taxonomy and organizes the accompanying vulnerabilities—memory poisoning, tool misuse, reward hacking, emergent deception, and value drift—into a single framework. It then argues that defenses must stop being external filters and become internal decision constraints, formalized through a Constrained Markov Decision Process and realized in a proposed reflective architecture (R2A2). If the argument holds, the practical consequence is that agent safety requires architects to build risk budgets and reflective oversight into the decision loop itself.

What carries the argument

The central object is the Constrained Markov Decision Process (CMDP): an MDP augmented with cost functions C_k and budgets d_k, where the agent maximizes discounted cumulative reward subject to cumulative costs staying within budgets. R2A2 is the paper's proposed architecture around this formalism—its risk-aware world model predicts future states and estimates both reward and risk, a constraint filter blocks actions that would violate budgets, and introspective reflection audits past decisions for misalignment. The load-bearing mechanism is the primal-dual update in Eq. (5): at each step the agent selects the action that maximizes a reward term plus a Lagrangian term penalizing expected constraint violation, then raises the penalty weights for any constraint currently exceeded. That mechanism is what converts the qualitative demand 'be safe' into a quantitative, optimizable requirement, and it is what would carry any formal safety guarantee the architecture can claim.

What would settle it

Run a controlled experiment where the same LLM agent faces adversarial memory poisoning and tool-misuse attacks under (a) prompt filtering only and (b) a CMDP-based reflective architecture with a learned cost model and constraint filter; if (b) does not yield fewer irreversible unsafe actions or a meaningful reward-risk trade-off than (a), the claim that formal constrained decision-making is the right vehicle for agent safety is not supported.

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Extended reading notes

Core claim

The paper's central claim is that each autonomy-enhancing capability—memory, recursive planning, tool invocation, self-reflection, multi-agent coordination—structurally expands the agent's decision model and thereby creates a distinct class of security failure that static, prompt-level defenses cannot address. It grounds this claim in the observation that memory breaks the Markov property, planning creates long-horizon error cascades, tools translate symbolic errors into real-world effects, reflection introduces non-stationary self-modification, and communication couples agents' states. To make safety tractable, the paper models the agent as a Constrained Markov Decision Process and asserts that risk can be represented as cost functions with budgets, so that a policy maximizing reward while satisfying all constraints is the correct target. On this basis, it proposes R2A2, which combines a risk-aware world model, a constraint filter in front of every action, and introspective reflection, and claims such an architecture can in principle converge to an ε-feasible policy rather than merely a heuristic safe-looking one.

Load-bearing premise

The load-bearing premise is that qualitative, open-ended risks like deception, value drift, and emergent misalignment can be encoded as numeric constraint cost functions C_k with fixed budgets d_k inside a CMDP, so that the primal-dual update genuinely produces deployable ε-feasible policies.

Editorial extensions

If this is right

  • Input sanitization and prompt filtering become necessary but not sufficient; safety must live in a constraint layer that evaluates actions before execution.
  • Memory systems designed under this view will need lifecycle controls—aging, invalidation, provenance checks—so that poisoned or stale entries cannot drive later decisions.
  • Tool use should be gated by actuation-boundedness and causal-grounding constraints, not just human approval at the end of a long chain.
  • Self-reflective agents should be built with bounded self-modification, external veto layers, and audit trails so that reflection cannot silently entrench a misaligned policy.
  • Multi-agent systems need communication grounding and trust calibration, because a single poisoned message can propagate through shared memory and consensus.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Possible next step: a direct benchmark comparing an R2A2-style CMDP agent against prompt-level baselines under identical memory poisoning and tool-misuse attacks, measuring unsafe-action rates per episode.
  • The taxonomy suggests a scaling hypothesis: as agents move from L3 to L5, successful attacks should become rarer but individually more damaging; collecting attack-success and impact-severity statistics across the levels would test that.
  • If the CMDP encoding is the bottleneck, then progress in this line depends more on specification of cost functions for semantic risks (deception, value drift) than on the optimization machinery itself.
  • The dual-reward arbitration idea also suggests a near-term deployment path: use human approval signals as a soft constraint layer even before provable cost models exist.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript surveys security risks of large-model-based autonomous agents. It proposes a five-level autonomy taxonomy (L1-L5), maps capabilities such as memory, planning, tool use, self-reflection, and multi-agent coordination to vulnerabilities, adopts a Constrained Markov Decision Process (CMDP) formalism to model safety constraints, and reviews defenses across perception, memory, planning, execution, and feedback layers. It then introduces R2A2 (Reflective Risk-Aware Agent Architecture), a modular design incorporating risk-aware world modeling, constraint filtering, and introspective reflection, and claims that a primal-dual CMDP update in Eq. (5) provides quantitative safety guarantees for real-world deployment. The survey is broad and well structured, but the central formal guarantee for R2A2 is not established by the presented equations, and the architecture is not experimentally validated.

Significance. If the R2A2 architecture and its formal guarantees were valid, the paper would make a useful contribution by shifting agent safety from external filtering to architecture-internal constraint handling, and by organizing a scattered literature into a coherent risk taxonomy. The strengths of the manuscript are its comprehensive citation coverage, the structured L1-L5 risk matrices in Tables 1-4, and the clear articulation of temporally extended, cross-module failure modes. However, the paper's distinctive claim of quantitative epsilon-feasible safety guarantees via Eq. (5) rests on a dual update that does not correspond to the stated trajectory-level constraint, and no implementation or empirical evaluation is provided. The survey value is real, but the central contribution as stated is not currently supported.

major comments (4)
  1. [Section 4.3, Eq. (5)] The claimed epsilon-feasible guarantee does not follow from the stated update. The constraint in Eq. (4) is a trajectory-level bound E_pi[sum_{t>=0} gamma^t C_k(s_t,a_t)] <= d_k, but the dual update compares Q_C(s_t,a_t), the discounted cost value from the current state-action pair, with the full budget d. Q_C is not an unbiased estimator of the constraint return J_C(pi) or of its slack, so the update is not a stochastic subgradient of the Lagrangian of Eq. (4). In a long-horizon episode, Q_C decreases as the remaining horizon shrinks, the positive-part term vanishes, and lambda is driven to zero before later violations can be accumulated; total cost can exceed d while lambda keeps shrinking. The cited convergence result [164] applies to constrained RL with a trajectory-level dual update and does not repair this mismatch. Please correct the update to evaluate the trajectory-level constraint, or remove the quantitative safety-guarantee claim and present R2A2 as a qualitative architecture proposal.
  2. [Section 4.3, Eqs. (4)-(5)] The primal-dual control mechanism is underspecified. No primal policy improvement step, learning rule, or update for Q_R and Q_C is given; the action selection arg max_a [Q_R(s,a) + <lambda_t, d - Q_C(s,a)>] presumes access to accurate reward and cost value functions that are not specified as learned or provided. Without these components, the convergence statement cannot be evaluated, and the phrase 'under standard conditions (e.g., bounded cost, Lipschitz continuity)' is too vague to justify the quoted epsilon-feasibility. Please provide the full algorithm or explicitly label the equations as a structural sketch rather than a proven mechanism.
  3. [Abstract and Section 4.3] R2A2 is described as providing 'quantitative safety guarantees for real-world deployment,' but the manuscript contains no implementation, experiments, or case studies that exercise the architecture. Because the paper is a survey, proposing a forward-looking architecture is within scope, but the guarantee claim should be made conditional on the formal mechanism being correct and on empirical validation. Please rephrase the abstract, Section 1.1, and Section 5 to distinguish the proposed design from an established, validated system, and add an explicit limitations statement.
  4. [Section 3.3 and Table 3] The mapping from qualitative autonomy-induced risks (deception, value drift, emergent misalignment) to concrete CMDP cost functions C_k with budgets d_k is asserted but not substantiated. For the CMDP formulation to underwrite R2A2's safety claims, the paper should discuss how such cost functions can be specified, learned, or calibrated for LLM-agent states, and what the representational limits are. As written, the formal model's applicability to the surveyed semantic risks is assumed rather than demonstrated, and this is precisely the step on which the epsilon-feasibility guarantee depends.
minor comments (5)
  1. [Figure 1] The label 'Statical Learning' should read 'Statistical Learning'.
  2. [Eq. (5)] The dual update writes d without a subscript k; please clarify that the update is applied coordinate-wise for each constraint k, with lambda = (lambda_1, ..., lambda_K).
  3. [Section 4.2.3] The description of RiskTransformer, Elastic Decision Transformer, and ARDT would benefit from specifying which of these actually implement a CMDP constraint, since the survey groups them as 'CMDP-based approaches' without formal detail.
  4. [References] Reference [14] is a Medium blog post; consider replacing it with a more permanent or peer-reviewed source.
  5. [Table 2] In the 'Action Interface' row, the claim that standalone LLMs have indirect effects is cited to [81] and [93]; [93] is a red-teaming paper that does not directly support that specific claim, so a more targeted citation would be preferable.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation found; the CMDP update rests on an external convergence result, and self-citations are supporting rather than load-bearing.

full rationale

The paper's central formal claim is the R2A2 CMDP formulation and the primal–dual update in Eq. (5). This update is presented as a standard Lagrangian relaxation and its ε-feasible convergence guarantee is attributed to reference [164] (Paternain et al., an external constrained-RL convergence result), not to the authors' own prior work. No fitted parameter is renamed as a prediction, and no equation is shown to reduce to its own input by construction. The self-citations that appear (e.g., MultiTrust [86], Evil Geniuses [135], STAIR [168], RealSafe-R1 [169], MLA-Trust [37]) are used as supporting examples in survey tables, defense-stack reviews, or future-work discussions; none supplies the logical content of Eq. (4)–(5) or the R2A2 architecture. A skeptical concern about whether Eq. (5)'s per-step dual update is a valid estimator of the trajectory-level constraint is a correctness objection to the cited guarantee, not a circularity: it does not show that the conclusion is equivalent to its premises by construction. The R2A2 components — risk-aware world modeling, constraint filtering, and reflection — are proposed as a design synthesis rather than derived from a dataset they are then used to predict. Under the stated evidence, the derivation chain is self-contained apart from non-load-bearing self-citations, so no circular step is identified.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The paper introduces no fitted numerical parameters because it contains no experiments. Its symbolic free parameters are the constraint budgets and learning rate that any implementation would need to specify. The main load-bearing assumptions are that CMDPs can encode qualitative agent-safety concerns and that standard primal-dual convergence applies to LLM-based agents. The only invented entity is the unvalidated R2A2 architecture.

free parameters (2)
  • constraint budgets d_k
    Symbolic budgets in the CMDP formulation, Section 3.3 and Eq. (4). No values are specified, and deployment would require hand-tuning or estimation for each safety constraint.
  • dual learning rate eta
    Symbolic learning rate in the primal-dual update, Eq. (5). No value is provided, and convergence behavior depends on its choice.
assumptions (4)
  • domain assumption CMDPs are a minimally sufficient formalism for modeling LLM-agent safety risks.
    Section 3.3 states that CMDPs provide a 'principled and minimally sufficient formalism' for the expanding risk surface, assuming safety risks can be captured by constraint costs and budgets.
  • standard math Primal-dual constrained optimization converges to an epsilon-feasible policy under standard assumptions.
    Section 4.3, Eq. (5) invokes convergence to an epsilon-feasible policy under conditions such as bounded cost and Lipschitz continuity, citing Paternain et al. [164]. The paper does not show these conditions hold for LLM agents.
  • domain assumption Increasing agent autonomy monotonically escalates and qualitatively transforms security risk.
    Section 2 and Table 4 build the taxonomy on the premise that each autonomy level introduces new capability that expands the attack surface.
  • domain assumption LLM agents can perform irreversible external actions that turn inference errors into material harm.
    Section 3.3.3 assumes that tool invocation and actuation create irreversible consequences such as file deletion or API misuse, which motivates the R2A2 constraint-filter design.
invented entities (1)
  • R2A2 (Reflective Risk-Aware Agent Architecture)
    purpose: A proposed unified safety architecture embedding CMDP constraints, a risk-aware world model, a constraint filter, and introspective reflection into LLM agents.
    R2A2 is described in Section 4.3 and Figure 4 but is not implemented, benchmarked, or evaluated. It offers no falsifiable prediction outside the paper.

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Cite this review

Pith. "Pith review of A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents." pith.science (2026). https://pith.science/paper/B2PLYHCB

@misc{pith2026250623844,
  author       = {Pith},
  title        = {Pith review of: A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B2PLYHCB}},
  note         = {Machine review of arXiv:2506.23844}
}
read the original abstract

Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents capable of perceiving, reasoning, and acting in dynamic, open-ended environments. These large-model agents mark a paradigm shift from static inference systems to interactive, memory-augmented entities. While these capabilities significantly expand the functional scope of AI, they also introduce qualitatively novel security risks - such as memory poisoning, tool misuse, reward hacking, and emergent misalignment - that extend beyond the threat models of conventional systems or standalone LLMs. In this survey, we first examine the structural foundations and key capabilities that underpin increasing levels of agent autonomy, including long-term memory retention, modular tool use, recursive planning, and reflective reasoning. We then analyze the corresponding security vulnerabilities across the agent stack, identifying failure modes such as deferred decision hazards, irreversible tool chains, and deceptive behaviors arising from internal state drift or value misalignment. These risks are traced to architectural fragilities that emerge across perception, cognition, memory, and action modules. To address these challenges, we systematically review recent defense strategies deployed at different autonomy layers, including input sanitization, memory lifecycle control, constrained decision-making, structured tool invocation, and introspective reflection. We introduce the Reflective Risk-Aware Agent Architecture (R2A2), a unified cognitive framework grounded in Constrained Markov Decision Processes (CMDPs), which incorporates risk-aware world modeling, meta-policy adaptation, and joint reward-risk optimization to enable principled, proactive safety across the agent's decision-making loop.

Figures

Figures reproduced from arXiv: 2506.23844 by the authors.

Figure 1
Figure 1. Historical evolution of AI agent paradigms. The development trajectory is categorized into the Pre-LLM Stage (1950s–2020s), encompassing [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. A five-level taxonomy of autonomy in LLM-based agents, illustrating capability evolution and risk expansion. The classification spans [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Modular architecture of an LLM-based agent with annotated vul [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The Reflective Risk-Aware Agent Architecture (R2A2). The agent integrates a large language model (LLM)-driven reasoning core with a risk-aware world model for long-horizon planning under con￾straints. Perceptual inputs are processed into latent state representa￾tions b…

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Forward citations

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Reference graph

Works this paper leans on

169 extracted references · 15 canonical work pages · cited by 6 Pith papers

  1. [164]

    Con- strained reinforcement learning has zero duality gap,

    S. Paternain, K. Shanmugam, A. Krause, and A. Ribeiro, “Con- strained reinforcement learning has zero duality gap,” in Ad- vances in Neural Information Processing Systems , vol. 32, 2019

    2019

  2. [1]

    AI agents vs. agentic AI: A conceptual taxonomy, applications and challenge,

    R. Sapkota, K. I. Roumeliotis, and M. Karkee, “AI agents vs. agentic AI: A conceptual taxonomy, applications and challenge,” arXiv preprint arXiv:2505.10468, 2025

    arXiv 2025

  3. [2]

    The rise and potential of large language model based agents: A survey,

    Z. Xi, W. Chen, X. Guo, W. He, Y. Ding, B. Hong, M. Zhang, J. Wang, S. Jin, E. Zhou, R. Zheng, X. Fan, X. Wang, L. Xiong, Y. Zhou, W. Wang, C. Jiang, Y. Zou, X. Liu, Z. Yin, S. Dou, R. Weng, W. Cheng, Q. Zhang, W. Qin, Y. Zheng, X. Qiu, X. Huang, and T. Gui, “The rise and potential of large language model based agents: A survey,” arXiv preprint arXiv:2309...

    arXiv 2023

  4. [3]

    On the opportunities and risks of foundation models,

    R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill et al. , “On the opportunities and risks of foundation models,” arXiv preprint arXiv:2108.07258, 2021

    arXiv 2021

  5. [4]

    GPT-4 technical report,

    OpenAI, “GPT-4 technical report,” arXiv preprint arXiv:2303.08774, 2023

    arXiv 2023

  6. [5]

    The llama 3 herd of models,

    A. Grattafiori, A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al- Dahle, A. Letman, A. Mathur, A. Schelten, A. Vaughanet al., “The llama 3 herd of models,” arXiv preprint arXiv:2407.21783, 2024

    arXiv 2024

  7. [6]

    React: Synergizing reasoning and acting in language models,

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” in International Conference on Learning Representations , 2023

    2023

  8. [7]

    Tool- former: Language models can teach themselves to use tools,

    T. Schick, J. Dwivedi-Yu, R. Dess `ı, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda, and T. Scialom, “Tool- former: Language models can teach themselves to use tools,” Advances in Neural Information Processing Systems , vol. 36, pp. 68 539–68 551, 2023

    2023

Show all 169 references
  1. [8]

    We- bgpt: Browser-assisted question-answering with human feed- back,

    R. Nakano, J. Hilton, S. Balaji, L. Ouyang, J. Wu, C. Kim, C. Hesse, S. Jain, V . Kosaraju, W. Saunders, X. Jiang, K. Cobbe, T. Eloundou, G. Krueger, K. Button, M. Knight, B. Chess, and J. Schulman, “We- bgpt: Browser-assisted question-answering with human feed- back,” arXiv p...

    2021 arXiv

  2. [9]

    Voyager: An open-ended embodied agent with large language models,

    G. Wang, Y. Xie, Y. Jiang, A. Mandlekar, C. Xiao, Y. Zhu, L. Fan, and A. Anandkumar, “Voyager: An open-ended embodied agent with large language models,” arXiv preprint arXiv:2305.16291 , 2023

    2023 arXiv

  3. [10]

    Auto-GPT for online decision making: Benchmarks and additional opinions,

    H. Yang, Y. Sifu, and Y. He, “Auto-GPT for online decision making: Benchmarks and additional opinions,” arXiv preprint arXiv:2306.02224, 2023

    2023 arXiv

  4. [11]

    Security of AI agents,

    Y. He, E. Wang, Y. Rong, Z. Cheng, and H. Chen, “Security of AI agents,” arXiv preprint arXiv:2406.08689, 2024

    2024 arXiv

  5. [12]

    Autogen: Enabling next-gen llm applications via multi-agent conversation,

    Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, L. Jiang, X. Zhang, S. Zhang, J. Liu et al. , “Autogen: Enabling next-gen llm applications via multi-agent conversation,” arXiv preprint arXiv:2308.08155, 2023

    2023 arXiv

  6. [13]

    Russell and P

    S. Russell and P . Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2021. IEEE TRANSACTIONS ON PATTERN ANAL YSIS AND MACHINE INTELLIGENCE 17

    2021

  7. [14]

    A comprehensive review of autonomous agents powered by large language models,

    N. Dunzelman, “A comprehensive review of autonomous agents powered by large language models,” Medium, 2023

    2023

  8. [15]

    A survey on large language model based autonomous agents,

    L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y. Lin, W. X. Zhao, Z. Wei, and J.-R. Wen, “A survey on large language model based autonomous agents,” arXiv preprint arXiv:2308.11432, 2023

    2023 arXiv

  9. [16]

    Large language model agent: A survey on methodology, applications and challenges,

    J. Luo, W. Zhang, and Y. e. a. Yuan, “Large language model agent: A survey on methodology, applications and challenges,” Nature Machine Intelligence, 2025

    2025

  10. [18]

    Managing Extreme AI Risks Amid Rapid Progress,

    Y. Bengio, G. Hinton, A. Yao, D. Song, P . Abbeel, T. Darrell, Y. N. Harari, Y.-Q. Zhang, L. Xue, S. Shalev-Shwartz, G. Hadfield, J. Clune, T. Maharaj, F. Hutter, A. G. Baydin, S. McIlraith, Q. Gao, A. Acharya, D. Krueger, A. Dragan, P . Torr, S. Russell, D. Kah- neman, J. Bra...

    2024

  11. [19]

    Intelligence explosion: Evi- dence and import,

    L. Muehlhauser and A. Salamon, “Intelligence explosion: Evi- dence and import,” In Singularity Hypotheses, pp. 15–42, 2012

    2012

  12. [20]

    The basic ai drives,

    S. M. Omohundro, “The basic ai drives,” in Artificial intelligence safety and security, 2018, pp. 47–55

    2018

  13. [21]

    Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?

    Y. Bengio, M. Cohen, D. Fornasiere, J. Ghosn, P . Greiner, M. Mac- Dermott, S. Mindermann, A. Oberman, J. Richardson, O. Richard- son, M.-A. Rondeau, P .-L. St-Charles, and D. Williams-King, “Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?...

    2025 arXiv

  14. [22]

    International Scientific Report on the Safety of Advanced AI: Interim Report,

    Y. e. a. Bengio, “International Scientific Report on the Safety of Advanced AI: Interim Report,” arXiv preprint arXiv:2412.05282 , 2024

    2024 arXiv

  15. [23]

    Human-artificial in- teraction in the age of agentic ai: a system-theoretical approach,

    U. M. Borghoff, P . Bottoni, and R. Pareschi, “Human-artificial in- teraction in the age of agentic ai: a system-theoretical approach,” Frontiers in Human Dynamics, vol. 7, p. 1579166, 2025

    2025

  16. [24]

    Agent foundations for aligning machine intelligence with human interests: a technical research agenda,

    N. Soares and B. Fallenstein, “Agent foundations for aligning machine intelligence with human interests: a technical research agenda,” The technological singularity: Managing the journey , pp. 103–125, 2017

    2017

  17. [25]

    Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents,

    Z. Wang, S. Cai, G. Chen, A. Liu, X. Ma, and Y. Liang, “Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents,” arXiv preprint arXiv:2302.01560, 2023

    2023 arXiv

  18. [26]

    AI agent security: Emerging threats and defense paradigms,

    Z. Deng, Y. Guo, C. Han, W. Ma, J. Xiong, S. Wen, and Y. Xiang, “AI agent security: Emerging threats and defense paradigms,” arXiv preprint arXiv:2408.29007, 2024

    2024

  19. [27]

    AdvLM: Visual adversarial attacks on autonomous driving vision-language models,

    T. Zhang, L. Wang, X. Zhang, Y. Zhang, B. Jia, S. Liang, S. Hu, Q. Fu, A. Liu, and X. Liu, “AdvLM: Visual adversarial attacks on autonomous driving vision-language models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, 2025, pp. 11 289–11 300

    2025

  20. [28]

    Agent Security Bench (ASB): Formalizing and benchmarking attacks and defenses in LLM-based agents,

    H. Zhang, J. Huang, K. Mei, Y. Yao, Z. Wang, C. Zhan, H. Wang, and Y. Zhang, “Agent Security Bench (ASB): Formalizing and benchmarking attacks and defenses in LLM-based agents,” arXiv preprint arXiv:2410.02644, 2024

    2024 arXiv

  21. [29]

    ASB: A security bench- mark for LLM-based autonomous agents,

    C. Li, H. Wang, W. Zhang, and M. Liu, “ASB: A security bench- mark for LLM-based autonomous agents,” IEEE Transactions on Dependable and Secure Computing , vol. 22, no. 3, pp. 1451–1468, 2025

    2025

  22. [30]

    Breaking agents: Compromising autonomous LLM agents through malfunction amplification,

    B. Zhang, Y. Tan, Y. Shen, A. Salem, M. Backes, S. Zannettou, and Y. Zhang, “Breaking agents: Compromising autonomous LLM agents through malfunction amplification,” arXiv preprint arXiv:2407.20859, 2024

    2024 arXiv

  23. [31]

    TrustAgent: Towards safe and trustworthy LLM-based agents,

    W. Hua, X. Yang, M. Jin, Z. Li, W. Cheng, R. Tang, and Y. Zhang, “TrustAgent: Towards safe and trustworthy LLM-based agents,” Findings of the Association for Computational Linguistics: EMNLP , 2024

    2024

  24. [32]

    A survey on trustwor- thy llm agents: Threats and countermeasures,

    M. Yu, F. Meng, X. Zhou, S. Wang, J. Mao, L. Pang, T. Chen, K. Wang, X. Li, Y. Zhang et al. , “A survey on trustwor- thy llm agents: Threats and countermeasures,” arXiv preprint arXiv:2503.09648, 2025

    2025 arXiv

  25. [33]

    Commercial llm agents are already vulnerable to simple yet dangerous attacks,

    A. Li, Y. Zhou, V . C. Raghuram, T. Goldstein, and M. Goldblum, “Commercial llm agents are already vulnerable to simple yet dangerous attacks,” arXiv preprint arXiv:2502.08586, 2025

    2025 arXiv

  26. [34]

    The obvious invisible threat: Llm-powered gui agents’ vulnerability to fine-print injections,

    C. Chen, Z. Zhang, B. Guo, S. Ma, I. Khalilov, S. A. Gebreegziab- her, Y. Ye, Z. Xiao, Y. Yao, T. Liet al., “The obvious invisible threat: Llm-powered gui agents’ vulnerability to fine-print injections,” arXiv preprint arXiv:2504.11281, 2025

    2025 arXiv

  27. [35]

    Securing agentic ai: A compre- hensive threat model and mitigation framework for generative ai agents,

    V . S. Narajala and O. Narayan, “Securing agentic ai: A compre- hensive threat model and mitigation framework for generative ai agents,” arXiv preprint arXiv:2504.19956, 2025

    2025 arXiv

  28. [36]

    Exploring large language model based intelligent agents: Definitions, methods, and prospects,

    Y. Cheng, C. Zhang, Z. Zhang, X. Meng, S. Hong, W. Li, Z. Wang, Z. Wang, F. Yin, J. Zhao et al., “Exploring large language model based intelligent agents: Definitions, methods, and prospects,” arXiv preprint arXiv:2401.03428, 2024

    2024 arXiv

  29. [37]

    Mla-trust: Benchmarking trustworthiness of multimodal llm agents in gui environments,

    X. Yang, J. Chen, J. Luo, Z. Fang, Y. Dong, H. Su, and J. Zhu, “Mla-trust: Benchmarking trustworthiness of multimodal llm agents in gui environments,” arXiv preprint arXiv:2506.01616 , 2025

    2025 arXiv

  30. [38]

    Alignment challenges in autonomous language agents,

    O. S. Team, “Alignment challenges in autonomous language agents,” arXiv preprint arXiv:2403.17815, 2024

    2024 arXiv

  31. [39]

    Caution for the environment: Multimodal agents are susceptible to environmental distractions,

    X. Ma, Y. Wang, Y. Yao, T. Yuan, A. Zhang, Z. Zhang, and H. Zhao, “Caution for the environment: Multimodal agents are susceptible to environmental distractions,” arXiv preprint arXiv:2408.02544 , 2024

    2024 arXiv

  32. [40]

    AgentDojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents,

    E. Debenedetti et al. , “AgentDojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents,” arXiv preprint arXiv:2406.13352, 2024

    2024 arXiv

  33. [41]

    Towards action hijacking of large language model-based agent,

    Y. Zhang, K. Chen, X. Jiang, Y. Sun, R. Wang, and L. Wang, “Towards action hijacking of large language model-based agent,” arXiv preprint arXiv:2412.10807, 2024

    2024 arXiv

  34. [42]

    AgentPoison: Red-teaming LLM agents via poisoning memory or knowledge bases,

    Z. Chen, Z. Xiang, C. Xiao, D. Song, and B. Li, “AgentPoison: Red-teaming LLM agents via poisoning memory or knowledge bases,” Advances in Neural Information Processing Systems , vol. 37, pp. 130 185–130 213, 2024

    2024

  35. [43]

    In prospect and retrospect: Reflective memory management for long-term personalized dialogue agents,

    Z. Tan, J. Yan, I.-H. Hsu, R. Han, Z. Wang, L. T. Le, Y. Song, Y. Chen, H. Palangi, G. Lee, A. Iyer, T. Chen, H. Liu, C.-Y. Lee, and T. Pfister, “In prospect and retrospect: Reflective memory management for long-term personalized dialogue agents,” arXiv preprint arXiv:2503.08026, 2025

    2025 arXiv

  36. [44]

    From exploration to mastery: Enabling LLMs to master tools via self-driven interac- tions,

    C. Qu, S. Dai, X. Wei, H. Cai, and S. Wu, “From exploration to mastery: Enabling LLMs to master tools via self-driven interac- tions,” arXiv preprint arXiv:2410.08197, 2024

    2024 arXiv

  37. [45]

    Technical risks of (lethal) autonomous weapons systems,

    H. Podar and A. Colijn, “Technical risks of (lethal) autonomous weapons systems,” arXiv preprint arXiv:2502.10174, 2025

    2025 arXiv

  38. [47]

    Tool-induced misalign- ment in llm agents: A causal analysis,

    B. Wang, X. Chen, Z. Wang, and C. Xiao, “Tool-induced misalign- ment in llm agents: A causal analysis,” in International Conference on Learning Representations, 2025

    2025

  39. [48]

    Safeguarding ai agents: Developing and analyzing safety architectures,

    I. Domkundwar, M. N S, and I. Bhola, “Safeguarding ai agents: Developing and analyzing safety architectures,” arXiv preprint arXiv:2409.03793, 2024

    2024 arXiv

  40. [49]

    Levels of AGI for op- erationalizing progress on the path to AGI,

    M. R. Morris, J. Sohl-Dickstein, N. Fiedel, T. Warkentin, A. Dafoe, A. Faust, C. Farabet, and S. Legg, “Levels of AGI for op- erationalizing progress on the path to AGI,” arXiv preprint arXiv:2311.02462, 2023

    2023

  41. [50]

    Measuring AI agent autonomy: Towards a scalable approach with code inspection,

    P . Cihon, M. Stein, G. Bansal, S. Manning, and K. Xu, “Measuring AI agent autonomy: Towards a scalable approach with code inspection,” arXiv preprint arXiv:2502.15212, 2025

    2025 arXiv

  42. [51]

    Levels of autonomy: Liability in the age of AI agents,

    L. Soder, J. Smakman, C. Dunlop, W. Pan, S. Swaroop, and N. Kolt, “Levels of autonomy: Liability in the age of AI agents,” in Workshop on Socially Responsible Language Modelling Research , 2025

    2025

  43. [52]

    Unveiling privacy risks in llm agent memory,

    B. Wang, W. He, P . He, S. Zeng, Z. Xiang, Y. Xing, and J. Tang, “Unveiling privacy risks in llm agent memory,” arXiv preprint arXiv:2502.13172, 2025

    2025 arXiv

  44. [53]

    The landscape of emerg- ing ai agent architectures for reasoning, planning, and tool call- ing: A survey,

    T. Masterman, A. Freed, and L. Weng, “The landscape of emerg- ing ai agent architectures for reasoning, planning, and tool call- ing: A survey,” arXiv preprint arXiv:2404.11584, 2024

    2024 arXiv

  45. [54]

    Tptu: Task planning and tool usage of large language model-based ai agents,

    J. Ruan, Y. Chen, B. Zhang, Z. Xu, T. Bao, H. Mao, Z. Li, X. Zeng, R. Zhao et al. , “Tptu: Task planning and tool usage of large language model-based ai agents,” in NeurIPS 2023 Foundation Models for Decision Making Workshop, 2023

    2023

  46. [55]

    Chemcrow: Augmenting large-language models with chemistry tools,

    A. M. Bran, S. Cox, O. Schilter, C. Baldassari, A. D. White, and P . Schwaller, “Chemcrow: Augmenting large-language models with chemistry tools,” arXiv preprint arXiv:2304.05376, 2023

    2023 arXiv

  47. [56]

    Dwim: Towards tool-aware visual reasoning via discrepancy-aware workflow generation & instruct-masking tuning,

    F. Ke, X. Leng, Z. Cai, Z. Khan, W. Wang, P . D. Haghighi, H. Rezatofighi, M. Chandraker et al., “Dwim: Towards tool-aware visual reasoning via discrepancy-aware workflow generation & instruct-masking tuning,” arXiv preprint arXiv:2503.19263, 2025

    2025

  48. [57]

    Agent-pro: Learning IEEE TRANSACTIONS ON PATTERN ANAL YSIS AND MACHINE INTELLIGENCE 18 to evolve via policy-level reflection and optimization,

    W. Zhang, K. Tang, H. Wu, M. Wang, Y. Shen, G. Hou, Z. Tan, P . Li, Y. Zhuang, and W. Lu, “Agent-pro: Learning IEEE TRANSACTIONS ON PATTERN ANAL YSIS AND MACHINE INTELLIGENCE 18 to evolve via policy-level reflection and optimization,” arXiv preprint arXiv:2402.17574, 2024

    2024 arXiv

  49. [58]

    Agent q: Advanced reasoning and learning for autonomous ai agents,

    P . Putta, E. Mills, N. Garg, S. Motwani, C. Finn, D. Garg, and R. Rafailov, “Agent q: Advanced reasoning and learning for autonomous ai agents,” arXiv preprint arXiv:2408.07199, 2024

    2024 arXiv

  50. [59]

    Ignore previous prompt: Attack techniques for language models,

    L. Perez and O. K. Hossain, “Ignore previous prompt: Attack techniques for language models,” arXiv preprint arXiv:2304.09701, 2023

    2023 arXiv

  51. [60]

    Universal and transferable ad- versarial attacks on aligned language models,

    J. Zou, T. Xiao, R. He et al. , “Universal and transferable ad- versarial attacks on aligned language models,” arXiv preprint arXiv:2307.15043, 2023

    2023 arXiv

  52. [61]

    Auto-gpt: An experimental open-source attempt to make gpt-4 fully autonomous,

    S. Gravitas, “Auto-gpt: An experimental open-source attempt to make gpt-4 fully autonomous,” 2023

    2023

  53. [62]

    Reflexion: Language agents with verbal reinforcement learn- ing,

    N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: Language agents with verbal reinforcement learn- ing,” Advances in Neural Information Processing Systems , vol. 36, pp. 8634–8652, 2023

    2023

  54. [63]

    World models and active inference,

    Y. Bengio, A. Goyal, and M. Halawa, “World models and active inference,” arXiv preprint arXiv:2302.06696, 2023

    2023 arXiv

  55. [64]

    Reinforcement learning with learned world models,

    L. Kirsch, E. Grefenstette, and M. Botvinick, “Reinforcement learning with learned world models,” in Advances in Neural Information Processing Systems (NeurIPS), 2021

    2021

  56. [65]

    Agi ruin: A list of lethalities,

    E. Yudkowsky, “Agi ruin: A list of lethalities,” 2023

    2023

  57. [66]

    Deep reinforcement learning from human preferences,

    P . F. Christiano, J. Leike, T. Brown et al. , “Deep reinforcement learning from human preferences,” in Advances in Neural Informa- tion Processing Systems (NeurIPS), 2017

    2017

  58. [67]

    Refusal- trained llms are easily jailbroken as browser agents,

    P . Kumar, E. Lau, S. Vijayakumar, T. Trinh, S. R. Team, E. Chang, V . Robinson, S. Hendryx, S. Zhou, M. Fredrikson et al., “Refusal- trained llms are easily jailbroken as browser agents,” arXiv preprint arXiv:2410.13886, 2024

    arXiv

  59. [68]

    R-judge: Benchmarking safety risk awareness for llm agents,

    T. Yuan, Z. He, L. Dong, Y. Wang, R. Zhao, T. Xia, L. Xu, B. Zhou, F. Li, Z. Zhang et al. , “R-judge: Benchmarking safety risk awareness for llm agents,” arXiv preprint arXiv:2401.10019 , 2024

    2024 arXiv

  60. [69]

    The effect of state represen- tation on llm agent behavior in dynamic routing games,

    L. Goodyear, R. Guo, and R. Johari, “The effect of state represen- tation on llm agent behavior in dynamic routing games,” arXiv preprint arXiv:2506.15624, 2025

    2025 arXiv

  61. [70]

    Agentalign: Navigating safety alignment in the shift from informative to agentic large language models,

    J. Zhang, L. Yin, Y. Zhou, and S. Hu, “Agentalign: Navigating safety alignment in the shift from informative to agentic large language models,” arXiv preprint arXiv:2505.23020, 2025

    2025 arXiv

  62. [71]

    Medical hallucinations in foundation models and their impact on healthcare,

    Y. Kim, H. Jeong, S. Chen, S. S. Li, M. Lu, K. Alhamoud, J. Mun, C. Grau, M. Jung, R. R. Gameiro, L. Fan, E. Park, T. Lin, J. Yoon, W. Yoon, M. Sap, Y. Tsvetkov, P . P . Liang, X. Xu, X. Liu, D. McDuff, H. Lee, H. W. Park, S. R. Tulebaev, and C. Breazeal, “Medical hallucinatio...

    2025

  63. [72]

    Fine-tuning language models from human preferences,

    D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Rad- ford, D. Amodei, P . Christiano, and G. Irving, “Fine-tuning language models from human preferences,” arXiv preprint arXiv:1909.08593, 2019

    1909 arXiv

  64. [73]

    Learning to sum- marize with human feedback,

    N. Stiennon, L. Ouyang, J. Wu, D. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P . F. Christiano, “Learning to sum- marize with human feedback,” Advances in neural information processing systems, vol. 33, pp. 3008–3021, 2020

    2020

  65. [74]

    Train- ing language models to follow instructions with human feed- back,

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P . Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al., “Train- ing language models to follow instructions with human feed- back,” Advances in neural information processing systems , vol. 35, pp. 27 730–27 744, 2022

    2022

  66. [75]

    Demonstrat- ing specification gaming in reasoning models,

    A. Bondarenko, D. Volk, D. Volkov, and J. Ladish, “Demonstrat- ing specification gaming in reasoning models,” arXiv preprint arXiv:2502.13295, 2025

    2025 arXiv

  67. [76]

    The emerged security and privacy of llm agent: A survey with case studies,

    F. He, T. Zhu, D. Ye, B. Liu, W. Zhou, and P . S. Yu, “The emerged security and privacy of llm agent: A survey with case studies,” arXiv preprint arXiv:2407.19354, 2024

    2024

  68. [77]

    Concrete problems in AI safety,

    D. Amodei, C. Olah, J. Steinhardt, P . Christiano, J. Schulman, and D. Man ´e, “Concrete problems in AI safety,” arXiv preprint arXiv:1606.06565, 2016

    2016 arXiv

  69. [78]

    Empowering working memory for large language model agents,

    J. Guo, N. Li, J. Qi, H. Yang, R. Li, Y. Feng, S. Zhang, and M. Xu, “Empowering working memory for large language model agents,” arXiv preprint arXiv:2312.17259, 2023

    2023 arXiv

  70. [79]

    OpenAI red teaming network,

    OpenAI, “OpenAI red teaming network,” OpenAI, Tech. Rep., 2023

    2023

  71. [80]

    A survey of red teaming in nlp: Attacks, evalua- tion, and defense,

    A. Chen et al., “A survey of red teaming in nlp: Attacks, evalua- tion, and defense,” arXiv preprint arXiv:2210.09819, 2022

    2022 arXiv

  72. [81]

    Ignore previous prompt: Attack tech- niques for language models,

    F. Perez and I. Ribeiro, “Ignore previous prompt: Attack tech- niques for language models,” arXiv preprint arXiv:2211.09527 , 2022

    2022 arXiv

  73. [82]

    A trembling house of cards? mapping adversarial attacks against language agents,

    L. Mo, Z. Liao, B. Zheng, Y. Su, C. Xiao, and H. Sun, “A trembling house of cards? mapping adversarial attacks against language agents,” arXiv preprint arXiv:2402.10196, 2024

    2024 arXiv

  74. [83]

    Badagent: Inserting and activating backdoor attacks in llm agents,

    Y. Wang, D. Xue, S. Zhang, and S. Qian, “Badagent: Inserting and activating backdoor attacks in llm agents,” arXiv preprint arXiv:2406.03007, 2024

    2024 arXiv

  75. [84]

    Adversarial examples: Attacks and defenses for deep learning,

    X. Yuan, P . He, Q. Zhu, and X. Li, “Adversarial examples: Attacks and defenses for deep learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2805–2824, 2019

    2019

  76. [85]

    Agentharm: A benchmark for measuring harmfulness of llm agents,

    M. Andriushchenko, A. Souly, M. Dziemian, D. Duenas, M. Lin, J. Wang, D. Hendrycks, A. Zou, Z. Kolter, M. Fredrikson et al. , “Agentharm: A benchmark for measuring harmfulness of llm agents,” arXiv preprint arXiv:2410.09024, 2024

    arXiv

  77. [86]

    Multitrust: A comprehensive benchmark towards trustworthy multimodal large language models,

    Y. Zhang, Y. Huang, Y. Sun, C. Liu, Z. Zhao, Z. Fang, Y. Wang, H. Chen, X. Yang, X. Wei, H. Su, Y. Dong, and J. Zhu, “Multitrust: A comprehensive benchmark towards trustworthy multimodal large language models,” in Advances in Neural Information Pro- cessing Systems, vol. 37, 2...

    2024

  78. [87]

    Sensor attack taxonomy and defense for cyber- physical systems,

    H. Wang et al., “Sensor attack taxonomy and defense for cyber- physical systems,” ACM Computing Surveys, 2023

    2023

  79. [88]

    Scav: Steering llm hallucination via self-checking augmented verification,

    Y. Zhou et al., “Scav: Steering llm hallucination via self-checking augmented verification,” arXiv preprint arXiv:2309.00247, 2023

    2023 arXiv

  80. [89]

    Extracting training data from large language models,

    N. Carlini et al. , “Extracting training data from large language models,” USENIX Security Symposium, 2021

    2021

  81. [90]

    Membership inference attacks on sequence-to- sequence models,

    C. Stadfeld et al., “Membership inference attacks on sequence-to- sequence models,” Proceedings of the ACM Conference on Computer and Communications Security (CCS), 2022

    2022

  82. [91]

    The surprising effectiveness of latent world models for continual reinforcement learning,

    S. Kessler, P . Miło ´s, J. Parker-Holder, and S. J. Roberts, “The surprising effectiveness of latent world models for continual reinforcement learning,” in Deep Reinforcement Learning Workshop NeurIPS 2022, 2022

    2022

  83. [92]

    Instruction tuning with gpt-4,

    B. Peng et al. , “Instruction tuning with gpt-4,” arXiv preprint arXiv:2304.03277, 2023

    2023 arXiv

  84. [93]

    Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned,

    D. Ganguli et al., “Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned,” arXiv preprint arXiv:2209.07858, 2022

    2022 arXiv

  85. [94]

    Retrieval-augmented language models expose sensitive information,

    A. Huang et al., “Retrieval-augmented language models expose sensitive information,” arXiv preprint arXiv:2310.01819, 2023

    2023 arXiv

  86. [95]

    Prompt injec- tion attack against LLM-integrated applications,

    Y. Liu, G. Deng, Y. Li, K. Wang, Z. Wang, X. Wang, T. Zhang, Y. Liu, H. Wang, Y. Zheng, and Y. Liu, “Prompt injec- tion attack against LLM-integrated applications,” arXiv preprint arXiv:2306.05499, 2023

    2023 arXiv

  87. [96]

    Benchmarking and defending against indirect prompt injection attacks on large language models,

    J. Yi, Y. Xie, B. Zhu, E. Kiciman, G. Sun, X. Xie, and F. Wu, “Benchmarking and defending against indirect prompt injection attacks on large language models,” in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’25), 2025

    2025

  88. [97]

    Breaking the ceiling: Exploring the potential of jailbreak attacks through expanding strategy space,

    Y. Huang, Y. Sun, S. Ruan, Y. Zhang, Y. Dong, and X. Wei, “Breaking the ceiling: Exploring the potential of jailbreak attacks through expanding strategy space,” arXiv preprint arXiv:2505.21277, 2025

    2025 arXiv

  89. [98]

    Attention tracker: Detecting prompt injection attacks in LLMs,

    K.-H. Hung, C.-Y. Ko, A. Rawat, I.-H. Chung, W. H. Hsu, and P .- Y. Chen, “Attention tracker: Detecting prompt injection attacks in LLMs,” in Findings of the Association for Computational Linguistics: NAACL 2025, 2025, pp. 2309–2322

    2025

  90. [99]

    Red teaming the mind of the machine: A systematic evaluation of prompt injection and jailbreak vulnerabilities in llms,

    C. Pathade, “Red teaming the mind of the machine: A systematic evaluation of prompt injection and jailbreak vulnerabilities in llms,” arXiv preprint arXiv:2505.04806, 2025

    2025 arXiv

  91. [100]

    How robust is google’s bard to adversarial image attacks?

    Y. Dong, H. Chen, J. Chen, Z. Fang, X. Yang, Y. Zhang, Y. Tian, H. Su, and J. Zhu, “How robust is google’s bard to adversarial image attacks?” in R0-FoMo Workshop on Robustness of Few-shot and Zero-shot Learning in Large Foundation Models in Advances in Neural Information Proc...

    2023

  92. [101]

    Adversarial attacks on multimodal agents,

    C. H. Wu, R. R. Shah, J. Y. Koh, R. Salakhutdinov, D. Fried, and A. Raghunathan, “Adversarial attacks on multimodal agents,” in Proceedings of the International Conference on Learning Representa- tions (ICLR 2025), 2025

    2025

  93. [102]

    Probing the robustness of vision–language pretrained mod- els: A multimodal adversarial attack approach,

    J. Guan, T. Ding, L. Cao, L. Pan, C. Wang, and X. Zheng, “Probing the robustness of vision–language pretrained mod- els: A multimodal adversarial attack approach,” arXiv preprint arXiv:2406.13461, 2024

    2024 arXiv

  94. [103]

    Guardian: Safeguarding llm multi-agent collaborations with temporal graph modeling,

    J. Zhou, L. Wang, and X. Yang, “Guardian: Safeguarding llm multi-agent collaborations with temporal graph modeling,”arXiv preprint arXiv:2505.19234, 2025. IEEE TRANSACTIONS ON PATTERN ANAL YSIS AND MACHINE INTELLIGENCE 19

    2025

  95. [104]

    Llm01:2025 prompt injection,

    OWASP Gen AI Security Project, “Llm01:2025 prompt injection,” Online technical report, 2025

    2025

  96. [105]

    Quantifying mis- alignment between agents: Towards a sociotechnical understand- ing of alignment,

    A. Kierans, H. Hazan, and S. Dori-Hacohen, “Quantifying mis- alignment between agents: Towards a sociotechnical understand- ing of alignment,” arXiv preprint arXiv:2406.04231, 2024

    2024 arXiv

  97. [106]

    A practical memory injection attack against llm agents,

    S. Dong, S. Xu, P . He, Y. Li, J. Tang, T. Liu, H. Liu, and Z. Xiang, “A practical memory injection attack against llm agents,” arXiv preprint arXiv:2503.03704, 2025

    2025

  98. [107]

    Memory matters: The need to improve long-term memory in llm-agents,

    K. Hatalis, D. Christou, J. Myers, S. Jones, K. Lambert, A. Amos- Binks, Z. Dannenhauer, and D. Dannenhauer, “Memory matters: The need to improve long-term memory in llm-agents,” Proceed- ings of the AAAI Symposium Series, vol. 2, no. 1, pp. 277–280, 2024

    2024

  99. [108]

    Bostrom, Superintelligence: Paths, Dangers, Strategies

    N. Bostrom, Superintelligence: Paths, Dangers, Strategies . Oxford University Press, 2014

    2014

  100. [109]

    Agent-safetybench: Evaluating the safety of llm agents,

    Z. Zhang, S. Cui, Y. Lu, J. Zhou, J. Yang, H. Wang, and M. Huang, “Agent-safetybench: Evaluating the safety of llm agents,” arXiv preprint arXiv:2412.14470, 2024

    2024 arXiv

  101. [110]

    Llm as os, agents as apps: Envisioning aios, agents and the aios-agent ecosystem,

    Y. Ge, Y. Ren, W. Hua, S. Xu, J. Tan, and Y. Zhang, “Llm as os, agents as apps: Envisioning aios, agents and the aios-agent ecosystem,” arXiv preprint arXiv:2312.03815, 2023

    2023 arXiv

  102. [111]

    Lan- guage models are few-shot learners,

    T. B. Brown, B. Mann, N. Ryder, M. Subbiah, and J. Kaplan, “Lan- guage models are few-shot learners,” in Proceedings of NeurIPS 2020, vol. 33, 2020, pp. 1877–1901

    2020

  103. [112]

    Limits to verification and validation of agentic behav- ior,

    D. J. Jilk, “Limits to verification and validation of agentic behav- ior,” in Artificial Intelligence Safety and Security, 2018, pp. 225–234

    2018

  104. [113]

    Teams of LLM agents can exploit zero-day vulnerabilities,

    R. Fang, R. Bindu, A. Gupta, Q. Zhan, and D. Kang, “Teams of LLM agents can exploit zero-day vulnerabilities,” arXiv preprint arXiv:2406.01637, 2024

    2024 arXiv

  105. [114]

    Cascading hallucination in multi-agent systems,

    Y. Liu, Y. Zhang, X. Wang, and D. Chen, “Cascading hallucination in multi-agent systems,” Nature Machine Intelligence, vol. 7, no. 3, pp. 145–156, 2025

    2025

  106. [115]

    Techniques and busi- ness perspectives llm-based multi-agent systems,

    Y. Zhang, L. Zhou, T. Schick, and J. Lin, “Techniques and busi- ness perspectives llm-based multi-agent systems,” arXiv preprint arXiv:2411.12076, 2024

    2024 arXiv

  107. [117]

    Altman, Constrained Markov Decision Processes

    E. Altman, Constrained Markov Decision Processes . CRC Press, 1999

    1999

  108. [118]

    A survey of safe reinforcement learning and constrained mdps: Foundations and extensions,

    A. Kushwaha, K. Ravish, P . Lamba, and P . Kumar, “A survey of safe reinforcement learning and constrained mdps: Foundations and extensions,” arXiv preprint arXiv:2505.17342, 2025

    2025 arXiv

  109. [119]

    Constrained policy optimization,

    J. Achiam, D. Held, A. Tamar, and P . Abbeel, “Constrained policy optimization,” in Proceedings of the 34th International Conference on Machine Learning (ICML), vol. 70, 2017, pp. 22–31

    2017

  110. [120]

    Benchmarking safe exploration in deep reinforcement learning,

    A. Ray, J. Achiam, and D. Amodei, “Benchmarking safe exploration in deep reinforcement learning,” arXiv preprint arXiv:1910.01708, 2019

    1910 arXiv

  111. [121]

    Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases,

    Z. Chen, Z. Xiang, C. Xiao, D. Song, and B. Li, “Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases,” NeurIPS (Poster), 2024

    2024

  112. [122]

    Imprompt: Prompt-based attacks on tool-using llm agents,

    X. Fu, Y. Song, and P . Liu, “Imprompt: Prompt-based attacks on tool-using llm agents,” arXiv preprint arXiv:2410.14923, 2024

    2024 arXiv

  113. [124]

    Watch out for your agents! investigating backdoor threats to LLM-based agents,

    W. Yang, X. Bi, Y. Lin, S. Chen, J. Zhou, and X. Sun, “Watch out for your agents! investigating backdoor threats to LLM-based agents,” arXiv preprint arXiv:2402.11208, 2024

    2024 arXiv

  114. [125]

    Breaking react agents: Foot-in-the-door attack will get you in,

    I. Nakash, G. Kour, G. Uziel, and A. Anaby-Tavor, “Breaking react agents: Foot-in-the-door attack will get you in,” arXiv preprint arXiv:2410.16950, 2024

    2024 arXiv

  115. [126]

    Multi-agent systems ex- ecute arbitrary malicious code,

    H. Triedman, R. Jha, and V . Shmatikov, “Multi-agent systems ex- ecute arbitrary malicious code,” arXiv preprint arXiv:2503.12188 , 2025

    2025 arXiv

  116. [127]

    Agentauditor: Human-level safety and security eval- uation for llm agents,

    H. Luo, S. Dai, C. Ni, X. Li, G. Zhang, K. Wang, T. Liu, and H. Salam, “Agentauditor: Human-level safety and security eval- uation for llm agents,” arXiv preprint arXiv:2506.00641, 2025

    2025

  117. [128]

    The effects of reward misspecification: Mapping and mitigating misaligned models,

    A. Pan, K. Bhatia, and J. Steinhardt, “The effects of reward misspecification: Mapping and mitigating misaligned models,” arXiv preprint arXiv:2201.03544, 2022

    2022 arXiv

  118. [129]

    Defining and characterizing reward hacking,

    J. Skalse, N. Howe, D. Krasheninnikov, and D. Krueger, “Defining and characterizing reward hacking,” arXiv preprint arXiv:2209.13085, 2022

    2022 arXiv

  119. [130]

    Reward hacking in reinforcement learning,

    L. Weng, “Reward hacking in reinforcement learning,” Lilian’s ML Blog, 2024

    2024

  120. [131]

    Mona: Myopic optimization with non-myopic approval can mitigate multi-step reward hacking,

    S. Farquhar, V . Varma, and D. e. a. Lindner, “Mona: Myopic optimization with non-myopic approval can mitigate multi-step reward hacking,” arXiv preprint arXiv:2501.13011, 2025

    2025 arXiv

  121. [132]

    Ai agents under threat: A survey of key security challenges and future pathways,

    Z. Deng, Y. Guo, C. Han, W. Ma, J. Xiong, S. Wen, and Y. Xiang, “Ai agents under threat: A survey of key security challenges and future pathways,” arXiv preprint arXiv:2406.02630, 2024

    2024

  122. [133]

    Debunc: Mitigating hallucinations in large language model agent communication with uncertainty estimations,

    L. Yoffe, A. Amayuelas, and W. Y. Wang, “Debunc: Mitigating hallucinations in large language model agent communication with uncertainty estimations,” arXiv preprint arXiv:2407.06426 , 2024

    2024 arXiv

  123. [134]

    Red- teaming llm multi-agent systems via communication attacks,

    P . He, Y. Lin, S. Dong, H. Xu, Y. Xing, and H. Liu, “Red- teaming llm multi-agent systems via communication attacks,” arXiv preprint arXiv:2502.14847, 2025

    2025 arXiv

  124. [135]

    Evil geniuses: Delving into the safety of llm-based agents,

    Y. Tian, X. Yang, J. Zhang, Y. Dong, and H. Su, “Evil geniuses: Delving into the safety of llm-based agents,” arXiv preprint arXiv:2311.11855, 2023

    2023 arXiv

  125. [136]

    A formal theory of inductive inference. part i,

    R. J. Solomonoff, “A formal theory of inductive inference. part i,” Information and Control, vol. 7, no. 1, pp. 1–22, 1964

    1964

  126. [137]

    Formalizing two problems of realistic world models,

    N. Soares and B. Fallenstein, “Formalizing two problems of realistic world models,” 2015

    2015

  127. [138]

    Questions of reasoning under logical uncertainty,

    N. Soares, B. Fallenstein, P . Christiano, and J. Taylor, “Questions of reasoning under logical uncertainty,” Technical report, 2015

    2015

  128. [139]

    Vila: On pre-training for visual language models,

    J. Lin, H. Yin, W. Ping, Y. Lu, P . Molchanov, A. Tao, H. Mao, J. Kautz, M. Shoeybi, and S. Han, “Vila: On pre-training for visual language models,” arXiv preprint arXiv:2312.07533, 2023

    2023 arXiv

  129. [140]

    Mat: Multimodal adversarial training for vision–language models,

    X. Chen, H. Li, R. Zhang, and M. Wang, “Mat: Multimodal adversarial training for vision–language models,” arXiv preprint arXiv:2405.18770, 2024

    2024

  130. [141]

    Struq: Defending against prompt injection with structured queries,

    S. Chen, J. Piet, C. Sitawarin, and D. Wagner, “Struq: Defending against prompt injection with structured queries,” arXiv preprint arXiv:2402.06363, 2024

    2024 arXiv

  131. [142]

    Prompt infection: LLM-to-LLM prompt injection within multi-agent systems,

    D. Lee and M. Tiwari, “Prompt infection: LLM-to-LLM prompt injection within multi-agent systems,” arXiv preprint arXiv:2410.07283, 2024

    2024 arXiv

  132. [143]

    Context-aware drift detection,

    O. Cobb and A. Van Looveren, “Context-aware drift detection,” in International Conference on Machine Learning. PMLR, 2022, pp. 4087–4111

    2022

  133. [144]

    Enhancing risk assess- ment in transformers with loss-at-risk functions,

    J. Zhang, H. Xie, X. Zhang, and K. Liu, “Enhancing risk assess- ment in transformers with loss-at-risk functions,” arXiv preprint arXiv:2411.02558, 2024

    2024 arXiv

  134. [145]

    Elastic decision trans- former,

    Y.-H. Wu, X. Wang, and M. Hamaya, “Elastic decision trans- former,” in Proceedings of the 2023 International Conference on Learning Representations (ICLR), 2023

    2023

  135. [146]

    Ad- versarially robust decision transformer,

    X. Tang, A. Marques, P . Kamalaruban, and I. Bogunovic, “Ad- versarially robust decision transformer,” in Proceedings of the 2024 International Conference on Learning Representations (ICLR), 2024

    2024

  136. [147]

    Risks from learned optimization in ad- vanced machine learning systems,

    E. Hubinger and et al., “Risks from learned optimization in ad- vanced machine learning systems,” Technical Report, MIRI, 2019

    2019

  137. [148]

    Process supervision: Aligning language models via intermediates,

    L. Luo, Y. Liu et al. , “Process supervision: Aligning language models via intermediates,” arXiv preprint arXiv:2406.06592, 2024

    2024 arXiv

  138. [149]

    Toolllm: Facilitating large language models to master 16,000+ real-world apis,

    Y. Qin, S. Liang, Y. Ye, K. Zhu, L. Yan, Y. Lu, Y. Lin, X. Cong, X. Tang, B. Qian, S. Zhao, L. Hong, R. Tian, R. Xie, J. Zhou, M. Gerstein, D. Li, Z. Liu, and M. Sun, “Toolllm: Facilitating large language models to master 16,000+ real-world apis,” in The 12th International Con...

    2024

  139. [150]

    Calm: Curiosity-driven auditing for large language models,

    X. Zheng, L. Wang, Y. Liu, X. Ma, C. Shen, and C. Wang, “Calm: Curiosity-driven auditing for large language models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 26, 2025, pp. 27 757–27 764

    2025

  140. [151]

    Rlaif: Reinforcement learning from human feed- back to mitigate reward hacking,

    K. Zhou et al., “Rlaif: Reinforcement learning from human feed- back to mitigate reward hacking,” in OpenReview (NeurIPS Work- shop), 2023

    2023

  141. [152]

    Fault self-healing: A biological immune heuristic reinforcement approach,

    Unknown, “Fault self-healing: A biological immune heuristic reinforcement approach,” Engineering Applications of Artificial In- telligence, 2024

    2024

  142. [153]

    Aligning llm agents by learning latent prefer- ences from user edits (prelude),

    T. Gao, A. Chen, B. Zhang, J. Liu, P . Li, P . Liang, and T. Hashimoto, “Aligning llm agents by learning latent prefer- ences from user edits (prelude),” in Proceedings of the 38th Confer- ence on Neural Information Processing Systems (NeurIPS) , 2024

    2024

  143. [154]

    Robust clip: Unsupervised adversarial fine-tuning of vision embeddings,

    C. Schlarmann, N. Singh, F. Croce, and M. Hein, “Robust clip: Unsupervised adversarial fine-tuning of vision embeddings,” arXiv preprint arXiv:2402.12336, 2024

    2024 arXiv

  144. [155]

    Evaluating the robustness of multimodal agents against active environmental injection attacks,

    Y. Chen, X. Hu, K. Yin, J. Li, and S. Zhang, “Evaluating the robustness of multimodal agents against active environmental injection attacks,” arXiv preprint arXiv:2502.13053, 2025. IEEE TRANSACTIONS ON PATTERN ANAL YSIS AND MACHINE INTELLIGENCE 20

    2025 arXiv

  145. [156]

    Survey of adversarial robustness in multimodal large language models,

    C. Jiang, Z. Wang, M. Dong, and J. Gui, “Survey of adversarial robustness in multimodal large language models,” arXiv preprint arXiv:2503.13962, 2025

    2025 arXiv

  146. [157]

    A-mem: Agentic memory for llm agents,

    M. Xu, T. Liang, S. Mei, and Z. Huang, “A-mem: Agentic memory for llm agents,” arXiv preprint arXiv:2502.12110, 2025

    2025 arXiv

  147. [158]

    Mini-llm: Memory-efficient structured pruning for large language models,

    H. Cheng, M. Zhang, and J. Q. Shi, “Mini-llm: Memory-efficient structured pruning for large language models,” arXiv preprint arXiv:2407.11681, 2024

    2024 arXiv

  148. [159]

    Dissecting adversarial robustness of multi- modal lm agents,

    H. C. Wu, R. Shah, J. Y. Koh, R. Salakhutdinov, D. Fried, and A. Raghunathan, “Dissecting adversarial robustness of multi- modal lm agents,” arXiv preprint arXiv:2406.12814, 2024

    2024 arXiv

  149. [160]

    Auditllm: a tool for auditing large language models using multiprobe approach,

    M. Amirizaniani, E. Martin, T. Roosta, A. Chadha, and C. Shah, “Auditllm: a tool for auditing large language models using multiprobe approach,” inProceedings of the 33rd ACM International Conference on Information and Knowledge Management , 2024, pp. 5174–5179

    2024

  150. [161]

    Riskq: Risk-sensitive multi-agent reinforcement learning value factorization,

    W. Sun, Q. Zhao, and X. Chen, “Riskq: Risk-sensitive multi-agent reinforcement learning value factorization,” in Advances in Neural Information Processing Systems (NeurIPS), 2023

    2023

  151. [162]

    Self-challenging language model agents,

    Y. Zhou, S. Levine, J. Weston, X. Li, and S. Sukhbaatar, “Self-challenging language model agents,” arXiv preprint arXiv:2506.01716, 2025

    2025 arXiv

  152. [163]

    Toolllm: Facilitating large lan- guage models to master 16000+ real-world apis,

    Y. Qin, S. Liang, Y. Ye, K. Zhu, L. Yan, Y. Lu, Y. Lin, X. Cong, X. Tang, B. Qian et al., “Toolllm: Facilitating large lan- guage models to master 16000+ real-world apis,” arXiv preprint arXiv:2307.16789, 2023

    2023 arXiv

  153. [165]

    Llm powered autonomous agents,

    L. Weng, “Llm powered autonomous agents,” 2023

    2023

  154. [166]

    Shieldagent: Shielding agents via verifiable safety policy reasoning,

    Z. Chen, M. Kang, and B. Li, “Shieldagent: Shielding agents via verifiable safety policy reasoning,” arXiv preprint arXiv:2503.22738, 2025

    2025

  155. [167]

    The fusion of large language models and formal methods for trustworthy ai agents: A roadmap,

    Y. Zhang, Y. Cai, X. Zuo, X. Luan, K. Wang, Z. Hou, Y. Zhang, Z. Wei, M. Sun, J. Sun, J. Sun, and J. S. Dong, “The fusion of large language models and formal methods for trustworthy ai agents: A roadmap,” arXiv preprint arXiv:2412.06512, 2024

    2024 arXiv

  156. [168]

    Stair: Improving safety alignment with introspective reasoning,

    Y. Zhang, S. Zhang, Y. Huang, Z. Xia, Z. Fang, X. Yang, R. Duan, D. Yan, Y. Dong, and J. Zhu, “Stair: Improving safety alignment with introspective reasoning,” arXiv preprint arXiv:2502.02384 , 2025

    2025 arXiv

  157. [169]

    Realsafe-r1: Safety-aligned deepseek-r1 without compromising reasoning capability,

    Y. Zhang, Z. Zeng, D. Li, Y. Huang, Z. Deng, and Y. Dong, “Realsafe-r1: Safety-aligned deepseek-r1 without compromising reasoning capability,” arXiv preprint arXiv:2504.10081, 2025

    2025 arXiv

  158. [170]

    Re-rest: Reflection-reinforced self-training for language agents,

    Z. Dou, C. Yang, X. Wu, K. Chang, and N. Peng, “Re-rest: Reflection-reinforced self-training for language agents,” arXiv preprint arXiv:2406.01495, 2024

    2024 arXiv

  159. [171]

    Aegisllm: Scaling agentic sys- tems for self-reflective defense in llm security,

    Z. Cai, S. Shabihi, B. An, Z. Che, B. R. Bartoldson, B. Kailkhura, T. Goldstein, and F. Huang, “Aegisllm: Scaling agentic sys- tems for self-reflective defense in llm security,” arXiv preprint arXiv:2504.20965, 2025

    2025 arXiv

  160. [172]

    Reflective multi-agent collaboration based on large language models,

    X. Bo, Z. Zhang, Q. Dai, X. Feng, L. Wang, R. Li, X. Chen, and J. Wen, “Reflective multi-agent collaboration based on large language models,” in Advances in Neural Information Processing Systems 37, 2024, pp. 13 859–13 875

    2024

  161. [173]

    Maebe: Multi-agent emergent behavior evaluation framework,

    S. Erisken, T. Gothard, M. Leitgab, and R. Potham, “Maebe: Multi-agent emergent behavior evaluation framework,” arXiv preprint arXiv:2506.03053, 2025

    2025 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.