pith. sign in

arxiv: 2606.12737 · v1 · pith:QLVPASIKnew · submitted 2026-06-10 · 💻 cs.CR · cs.AI

PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections

Pengfei He , Lesly Miculicich , Vishesh Sharma , Ash Fox , George Lee , Jiliang Tang , Tomas Pfister , Long T. Le This is my paper

Pith reviewed 2026-06-27 08:54 UTC · model grok-4.3

classification 💻 cs.CR cs.AI
keywords prompt injectionred-teamingLLM agentsvulnerability exposureautomated auditingindirect attacksattack evolution
0
0 comments X

The pith

PI-Hunter evolves source-aware test cases via feedback to expose how LLM agents retrieve and act on hidden malicious instructions from external sources.

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

The paper claims that existing red-teaming for prompt injection mainly measures attack success rates while leaving developers blind to the pathways by which indirect injections propagate through agentic systems. PI-Hunter addresses this gap by building test cases tied to specific external sources and then iteratively refining them based on the agent's own responses, pushing the agent to surface embedded instructions that would otherwise stay latent. A sympathetic reader would care because agent systems increasingly pull untrusted data into their reasoning loops, so a method that maps these exposure points could shift security work from reactive blocking to proactive pathway discovery. The experiments test this across benchmarks, architectures, attacks, and defenses to show gains in both the number and variety of vulnerabilities found.

Core claim

PI-Hunter constructs realistic source-aware test cases and iteratively evolves them through feedback-driven exploration to induce agents to retrieve and reveal latent malicious instructions embedded within external environments, substantially improving vulnerability exposure and attack-surface coverage over strong automated red-teaming baselines while remaining effective under existing prompt injection defenses.

What carries the argument

The feedback-driven evolution loop that starts with source-linked test cases and refines attack instances by observing whether the agent retrieves and follows embedded instructions.

If this is right

  • Developers obtain explicit maps of how specific external sources can trigger hidden instructions rather than only aggregate success rates.
  • Attack-surface coverage becomes measurable through the diversity of evolved test cases that reach different agent components.
  • Existing defenses can be evaluated against a wider and more adaptive set of injection attempts generated by the same evolution process.
  • Localization of vulnerable retrieval or reasoning steps inside the agent becomes feasible as a byproduct of the source-aware construction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same evolution mechanism could be applied to other agent risks such as unintended tool calls or data leakage by swapping the target instruction type.
  • Benchmarks that simulate external sources would need to be expanded with richer, time-varying content to keep the evolved attacks realistic.
  • Agent architectures that separate retrieval from reasoning might show systematically lower coverage under this auditing method, pointing to a testable design difference.

Load-bearing premise

The source-aware test cases and feedback-driven evolution produce attack instances that are representative of real-world indirect prompt injections through untrusted external sources.

What would settle it

Running the same agents in live environments with actual untrusted external data sources and checking whether the injection points and propagation paths identified by PI-Hunter match the attacks that succeed in those live settings.

read the original abstract

Large Language Models (LLMs) are rapidly evolving into agentic systems that interact with external tools and environments, introducing new security risks such as indirect prompt injection attacks through untrusted external sources. Existing defenses mainly focus on blocking malicious content at inference time, and current red-teaming methods primarily optimize attack success. As a result, developers have limited visibility into how latent prompt injections emerge and propagate through agents. We propose PI-Hunter, an automated agentic auditing framework for proactive vulnerability exposure in LLM agents. PI-Hunter constructs realistic source-aware test cases and iteratively evolves them through feedback-driven exploration to induce agents to retrieve and reveal latent malicious instructions embedded within external environments. Extensive experiments across multiple benchmarks, agent architectures, attacks, and defenses demonstrate that PI-Hunter substantially improves vulnerability exposure and attack-surface coverage over strong automated red-teaming baselines, while remaining effective under existing prompt injection defenses.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces PI-Hunter, an automated agentic auditing framework for proactive vulnerability exposure in LLM agents. It constructs source-aware test cases and iteratively evolves them through feedback-driven exploration to induce agents to retrieve and reveal latent malicious instructions embedded within external environments. The central claim is that extensive experiments across multiple benchmarks, agent architectures, attacks, and defenses demonstrate that PI-Hunter substantially improves vulnerability exposure and attack-surface coverage over strong automated red-teaming baselines, while remaining effective under existing prompt injection defenses.

Significance. If the empirical results are robust, this work would offer a practical advance in automated red-teaming for agentic LLM systems by shifting focus from inference-time blocking to proactive discovery and localization of indirect prompt injections. The source-aware construction and evolution mechanism could help developers gain visibility into attack propagation if the generated instances are shown to be representative.

major comments (2)
  1. Abstract: the claim that 'extensive experiments demonstrate improvement' provides no quantitative numbers, error bars, baseline selection criteria, or success metrics; this leaves the central empirical assertion unevaluated and load-bearing for the paper's contribution.
  2. The weakest assumption—that source-aware test-case construction and feedback-driven evolution produce representative real-world indirect prompt injections—is not accompanied by any validation (e.g., comparison to documented incidents or human review), which directly affects whether the reported coverage gains generalize.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract and the representativeness of our test cases. We address each major comment below and outline planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: Abstract: the claim that 'extensive experiments demonstrate improvement' provides no quantitative numbers, error bars, baseline selection criteria, or success metrics; this leaves the central empirical assertion unevaluated and load-bearing for the paper's contribution.

    Authors: We agree that the abstract would benefit from more concrete empirical details to support the central claim. The full manuscript (Sections 4–5) reports specific metrics including attack success rates, coverage improvements (e.g., percentage gains over baselines), error bars from multiple runs, and explicit baseline selection criteria. We will revise the abstract to incorporate key quantitative results while maintaining brevity. revision: yes

  2. Referee: The weakest assumption—that source-aware test-case construction and feedback-driven evolution produce representative real-world indirect prompt injections—is not accompanied by any validation (e.g., comparison to documented incidents or human review), which directly affects whether the reported coverage gains generalize.

    Authors: The source-aware test cases are derived from established attack patterns in the prompt injection literature and standard benchmarks used throughout the experiments. The iterative evolution is guided by agent feedback to simulate realistic retrieval and propagation. While the manuscript does not include direct comparisons to specific real-world incidents or human validation studies, the results demonstrate consistent performance across diverse agent architectures, attacks, and defenses. We will add a brief discussion in the limitations section addressing generalizability and the basis for realism claims. revision: partial

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper describes an empirical automated red-teaming framework evaluated via experiments across benchmarks, agent architectures, attacks, and defenses. No equations, fitted parameters, or derivation steps are present in the provided text. Claims of improved exposure and coverage rest on direct comparisons to baselines rather than any self-referential construction, self-citation chain, or ansatz that reduces to inputs by definition. The method is self-contained as an engineering contribution whose validity is assessed externally through reported metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the ledger is therefore empty.

pith-pipeline@v0.9.1-grok · 5705 in / 1108 out tokens · 11220 ms · 2026-06-27T08:54:48.081455+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

35 extracted references · 11 canonical work pages · 3 internal anchors

  1. [1]

    Association for Computational Linguistics , year=

    Red-teaming llm multi-agent systems via communication attacks , author=. Association for Computational Linguistics , year=

  2. [2]

    International Conference on Machine Learning , year=

    Promptbreeder: Self-referential self-improvement via prompt evolution , author=. International Conference on Machine Learning , year=

  3. [3]

    Findings of the Association for Computational Linguistics: ACL 2024 , pages=

    Injecagent: Benchmarking indirect prompt injections in tool-integrated large language model agents , author=. Findings of the Association for Computational Linguistics: ACL 2024 , pages=

    2024

  4. [4]

    Advances in Neural Information Processing Systems , volume=

    Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents , author=. Advances in Neural Information Processing Systems , volume=

  5. [5]

    AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?

    AgentDyn: A Dynamic Open-Ended Benchmark for Evaluating Prompt Injection Attacks of Real-World Agent Security System , author=. arXiv preprint arXiv:2602.03117 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [6]

    Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    Safesearch: Do not trade safety for utility in LLM search agents , author=. Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    2026

  7. [7]

    arXiv preprint arXiv:2410.01606 , year=

    Automated red teaming with goat: the generative offensive agent tester , author=. arXiv preprint arXiv:2410.01606 , year=

    work page arXiv

  8. [8]

    International Conference on Machine Learning , pages=

    Automated Red Teaming with GOAT: the Generative Offensive Agent Tester , author=. International Conference on Machine Learning , pages=. 2025 , organization=

    2025

  9. [9]

    Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning , author=. Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    2026

  10. [10]

    Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security , pages=

    Secalign: Defending against prompt injection with preference optimization , author=. Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security , pages=

    2025

  11. [11]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    The task shield: Enforcing task alignment to defend against indirect prompt injection in llm agents , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  12. [12]

    IsolateGPT: An execution isolation ar- chitecture for LLM-based agentic systems,

    Isolategpt: An execution isolation architecture for llm-based agentic systems , author=. arXiv preprint arXiv:2403.04960 , year=

    work page arXiv

  13. [13]

    International Conference on Machine Learning , pages=

    MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents , author=. International Conference on Machine Learning , pages=. 2025 , organization=

    2025

  14. [14]

    International Conference on Learning Representations (ICLR) , year =

    React: Synergizing reasoning and acting in language models , author=. International Conference on Learning Representations (ICLR) , year =

  15. [15]

    Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=

    Advancing Reasoning with Off-the-Shelf LLMs: A Semantic Structure Perspective , author=. Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=

    2025

  16. [16]

    First conference on language modeling , year=

    Autogen: Enabling next-gen LLM applications via multi-agent conversations , author=. First conference on language modeling , year=

  17. [17]

    Advances in neural information processing systems , volume=

    Reflexion: Language agents with verbal reinforcement learning , author=. Advances in neural information processing systems , volume=

  18. [18]

    LLM01: Prompt Injection , year =

  19. [19]

    arXiv preprint arXiv:2507.02735 , year=

    Meta secalign: A secure foundation llm against prompt injection attacks , author=. arXiv preprint arXiv:2507.02735 , year=

    work page arXiv

  20. [20]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    PIGuard: Prompt injection guardrail via mitigating overdefense for free , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  21. [21]

    arXiv preprint arXiv:2601.07072 , year=

    Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systems , author=. arXiv preprint arXiv:2601.07072 , year=

    work page arXiv

  22. [22]

    AgentSentry: Mitigating indirect prompt injection in LLM agents via temporal causal diagnostics and context purification,

    AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification , author=. arXiv preprint arXiv:2602.22724 , year=

    work page arXiv

  23. [23]

    International Conference on Machine Learning , year=

    CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution , author=. International Conference on Machine Learning , year=

  24. [24]

    Advances in neural information processing systems , volume=

    Toolformer: Language models can teach themselves to use tools , author=. Advances in neural information processing systems , volume=

  25. [25]

    Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

    Ehragent: Code empowers large language models for few-shot complex tabular reasoning on electronic health records , author=. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

    2024

  26. [26]

    International Conference on Learning Representations , volume=

    A real-world webagent with planning, long context understanding, and program synthesis , author=. International Conference on Learning Representations , volume=

  27. [27]

    arXiv preprint arXiv:2510.04550 , year=

    TRAJECT-Bench: A Trajectory-Aware Benchmark for Evaluating Agentic Tool Use , author=. arXiv preprint arXiv:2510.04550 , year=

    work page arXiv

  28. [28]

    arXiv preprint arXiv:2505.05849 , year=

    Agentvigil: Generic black-box red-teaming for indirect prompt injection against llm agents , author=. arXiv preprint arXiv:2505.05849 , year=

    work page arXiv

  29. [29]

    Advances in Neural Information Processing Systems , volume=

    Autoredteamer: Autonomous red teaming with lifelong attack integration , author=. Advances in Neural Information Processing Systems , volume=

  30. [30]

    Defending Against Indirect Prompt Injection Attacks With Spotlighting

    Defending against indirect prompt injection attacks with spotlighting , author=. arXiv preprint arXiv:2403.14720 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  31. [31]

    33rd USENIX Security Symposium (USENIX Security 24) , pages=

    Formalizing and benchmarking prompt injection attacks and defenses , author=. 33rd USENIX Security Symposium (USENIX Security 24) , pages=

  32. [32]

    Automatic and universal prompt injection attacks against large language models,

    Automatic and universal prompt injection attacks against large language models , author=. arXiv preprint arXiv:2403.04957 , year=

    work page arXiv

  33. [33]

    Prompt Injection attack against LLM-integrated Applications

    Prompt injection attack against llm-integrated applications , author=. arXiv preprint arXiv:2306.05499 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  34. [34]

    Proceedings of the 16th ACM workshop on artificial intelligence and security , pages=

    Not what you've signed up for: Compromising real-world llm-integrated applications with indirect prompt injection , author=. Proceedings of the 16th ACM workshop on artificial intelligence and security , pages=

  35. [35]

    Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    PEAR: Planner-Executor Agent Robustness Benchmark , author=. Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    2026