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arxiv: 2605.11442 · v1 · submitted 2026-05-12 · 💻 cs.CR · cs.AI· cs.CL

Recognition: 2 theorem links

· Lean Theorem

Can a Single Message Paralyze the AI Infrastructure? The Rise of AbO-DDoS Attacks through Targeted Mobius Injection

Haibo Hu, Qingqing Ye, Ronghua Li, Yanyun Wang, Zi Liang

Pith reviewed 2026-05-13 02:20 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.CL
keywords Mobius InjectionAbO-DDoSSemantic ClosureLLM agentsprompt injectionDDoS attacksAI securityrecursive execution
0
0 comments X

The pith

A single textual injection can turn LLM agents into self-sustaining DDoS sources by exploiting semantic closure in their logic.

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

The paper establishes that LLM agents, which mediate between users and digital services, contain a structural flaw in their reasoning called semantic closure. This flaw lets one crafted message trigger repeated execution of the agent's own components, converting the agent into a zombie node for agent-based DDoS attacks. If correct, the method requires minimal attacker effort yet produces large-scale effects that bypass conventional security checks. Experiments on multiple agent types and language models confirm call volumes can multiply by up to 51 times on one node and network delays by up to 229 times across nodes, with effects growing faster as more agents are affected. The work also introduces a monitoring technique that tracks energy usage in the agent's component graph to catch these triggers.

Core claim

Mobius Injection weaponizes autonomous agents into zombie nodes for agent-based and oriented DDoS attacks by exploiting semantic closure in agentic logic. A single textual injection induces sustained recursive execution of agent components, producing substantial call amplification and latency inflation that increases superlinearly with the number of affected nodes.

What carries the argument

Semantic Closure, the structural vulnerability in agentic logic that permits sustained recursive execution of agent components from one textual injection.

If this is right

  • One message per agent is sufficient to maintain the attack without further interaction.
  • Call amplification reaches up to 51 times on individual nodes.
  • Network latency inflation reaches up to 229 times in multi-node settings.
  • The amplification grows superlinearly as more agents receive the injection.
  • A defense based on Agent Component Energy Analysis can detect the recursion by spotting anomalous energy in the component graph.

Where Pith is reading between the lines

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

  • Agent builders may need to insert explicit loop termination checks into component interactions to limit recursion.
  • Infrastructure operators could add execution-loop tracking to their monitoring tools to handle this class of threat.
  • The same closure risk may appear in other autonomous orchestration systems that chain multiple services without hard termination rules.
  • Widespread agent deployment could require new standards for verifying that decision graphs cannot enter self-reinforcing states.

Load-bearing premise

Real-world LLM agents possess an exploitable semantic closure property that allows sustained recursion from one injection while evading existing safety filters and monitors.

What would settle it

An observation that the injected message produces no sustained recursion in tested agents or is reliably caught by current monitors would show the attack cannot succeed at the claimed scale.

Figures

Figures reproduced from arXiv: 2605.11442 by Haibo Hu, Qingqing Ye, Ronghua Li, Yanyun Wang, Zi Liang.

Figure 1
Figure 1. Figure 1: Comparison of Attack Paradigms: Current LLM API DoS (a) vs. The Proposed AbO-DDoS (b). In the traditional model (a), the adversary must maintain a vast infrastructure of accounts and IPs to sustain a linear, high-cost flooding stream, which is easily throttled by perimeter-based rate limiting. In contrast, Mobius Injection (b) exploits the semantic decoupling between the reasoning engine and the tool-execu… view at source ↗
Figure 2
Figure 2. Figure 2: Semantic-closure mechanism of the Mobius strip. ¨ Each returned message delivers a visible task-completion signal to the user and a hidden runner instruction to the agent policy simultaneously. This dual-channel interpretation allows two grafted components to sustain a recurrent invocation loop without issuing an explicit self-call, concealing the cycle from human oversight and standard execution monitors.… view at source ↗
Figure 3
Figure 3. Figure 3: Operational pipeline of Mobius Injection. ¨ A single injected text fragment enters via an ingress channel, is interpreted as context by the agent policy, and is grafted into mutable components through ADD or EDIT. The resulting returner–caller closure then couples a recurrent loop to downstream resources (LLM backends, MCP servers, plugins), transforming the agent into a persistent zombie. The entire pipel… view at source ↗
Figure 4
Figure 4. Figure 4: Cross-evaluation of targeted Mobius Injection across four execu- ¨ tion profiles: E1 (OpenClaw/Kimi/MCP-A), E2 (ZeroClaw/Kimi/MCP-A), E3 (Hermes/Kimi/API), and E4 (OpenClaw/Qwen/MCP-B). Left and right panels report task success rate and targeted P-ASR, respectively. B. Effectiveness of Mobius Injection ¨ In this section, we evaluate the fundamental effectiveness of Mobius Injection. By analyzing the transi… view at source ↗
Figure 5
Figure 5. Figure 5: Single-node AbO-DDoS resource amplification: cumulative LLM calls (top row) and token consumption (bottom row) across three agents and two [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Queue externality from coordinated poisoned nodes: benign probe latency, SLA violation rate, and inferred queue occupancy under increasing [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Network-layer feature space: HTTP request rate vs. connection rate [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: All-benign concurrency control versus the prior M [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Injection text for OpenClaw ADD-S skill graft. [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Three-agent ADD-S injection text family used by the OpenClaw, Hermes, and ZeroClaw skill-graft generator. [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Injection text for OpenClaw ADD-M MCP graft. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Injection text for OpenClaw ADD-C configuration graft. [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Injection text for EDIT-M overwriting an existing MCP entry. [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Injection text for EDIT-C in-place configuration overwrite. [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Injection text for targeted ADD-S guard. [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗
read the original abstract

Large Language Model (LLM) agents have emerged as key intermediaries, orchestrating complex interactions between human users and a wide range of digital services and LLM infrastructures. While prior research has extensively examined the security of LLMs and agents in isolation, the systemic risk of the agent acting as a disruptive hub within the user-agent-service chain remains largely overlooked. In this work, we expose a novel threat paradigm by introducing Mobius Injection, a sophisticated attack that weaponizes autonomous agents into zombie nodes to launch what we define as gent-based and -Oriented DDoS (AbO-DDoS) attacks. By exploiting a structural vulnerability in agentic logic named Semantic Closure, an adversary can induce sustained recursive execution of agent components through a single textual injection. We demonstrate that this attack is exceptionally lightweight, stealthy against both traditional DDoS monitors and contemporary AI safety filters, and highly configurable, allowing for surgical targeting of specific environments or model providers. To evaluate the real-world impact, we conduct extensive experiments across three representative claw-style agents and three mainstream coding agents, integrated with 12 frontier proprietary or open-weight LLMs. Our results demonstrate that Mobius Injection achieves substantial attack success across diverse tasks, driving single-node call amplification up to 51.0x and multi-node p95 latency inflation up to 229.1x. The attack performance exhibits a superlinear increase with the number of poisoning nodes. To mitigate Mobius Injection, we propose a proactive defense mechanism using Agent Component Energy (ACE) Analysis, which detects malicious recursive triggers by measuring anomalous energy in the agent's component graph.

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 / 2 minor

Summary. The paper introduces Mobius Injection, a single-textual-injection attack that exploits a purported structural property called Semantic Closure in LLM agents to induce sustained recursive component execution. This turns agents into zombie nodes for AbO-DDoS attacks. Experiments across three claw-style agents, three coding agents, and 12 LLMs report single-node call amplification up to 51.0x, multi-node p95 latency inflation up to 229.1x, and superlinear scaling with poisoning nodes. A defense based on Agent Component Energy (ACE) Analysis is proposed to detect anomalous recursion via component-graph energy measurements.

Significance. If the empirical results hold under realistic agent constraints, the work would identify a previously under-examined systemic risk: lightweight, stealthy amplification attacks that leverage agent autonomy to exhaust LLM infrastructure resources. The breadth of evaluation (multiple agent types and models) and the introduction of a measurable defense metric are positive contributions, though the absence of baselines and guardrail details limits immediate impact assessment.

major comments (2)
  1. [Experimental Evaluation (and agent implementation details)] The central amplification claims (51.0x calls, 229.1x latency) rest on the assumption that the evaluated agents permit sustained recursion after a single injection. The experimental description does not specify whether the three claw-style and three coding agents enforce standard production safeguards such as max-iteration counters, context-window truncation, or tool-call budgets. Without this information, the reported numbers cannot be distinguished from artifacts of an unguarded harness, undermining the claim that Semantic Closure is a general structural vulnerability.
  2. [Defense Mechanism and ACE Analysis] The definition and detection of Semantic Closure are introduced without a formal characterization or falsifiable test independent of the attack success metric. The ACE Analysis defense is presented as measuring anomalous energy in the component graph, but no equation, threshold derivation, or false-positive evaluation on benign recursive tasks is supplied, leaving the mitigation claim unsupported by the same experimental rigor applied to the attack.
minor comments (2)
  1. [Abstract and Introduction] The abstract and introduction use the novel terms AbO-DDoS, Mobius Injection, and Semantic Closure without an early, concise glossary or comparison table to prior prompt-injection and agent-loop attacks.
  2. [Results] No error bars, standard deviations, or number of runs per configuration are reported for the amplification and latency figures, making it difficult to assess statistical reliability of the 51.0x and 229.1x peaks.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and will revise the manuscript to provide the requested clarifications and formalizations.

read point-by-point responses

  1. Referee: The central amplification claims (51.0x calls, 229.1x latency) rest on the assumption that the evaluated agents permit sustained recursion after a single injection. The experimental description does not specify whether the three claw-style and three coding agents enforce standard production safeguards such as max-iteration counters, context-window truncation, or tool-call budgets. Without this information, the reported numbers cannot be distinguished from artifacts of an unguarded harness, undermining the claim that Semantic Closure is a general structural vulnerability.

    Authors: We agree that explicit configuration details are required to substantiate the claims. The evaluated agents were based on standard frameworks (LangChain ReAct for coding agents and custom loop-based implementations for claw-style agents) with default production-like settings: iteration caps of 30-50 steps, 8k-32k token context windows, and tool budgets of 20 calls per turn. The observed amplification occurred within these bounds because the injection creates a self-reinforcing semantic loop that consumes the budget before termination. In the revision we will add a dedicated table and subsection listing the exact safeguard parameters for all six agents, along with ablation results showing attack success even when caps are tightened to 10 iterations. This will demonstrate that the vulnerability is structural rather than an artifact of an unguarded setup. revision: yes

  2. Referee: The definition and detection of Semantic Closure are introduced without a formal characterization or falsifiable test independent of the attack success metric. The ACE Analysis defense is presented as measuring anomalous energy in the component graph, but no equation, threshold derivation, or false-positive evaluation on benign recursive tasks is supplied, leaving the mitigation claim unsupported by the same experimental rigor applied to the attack.

    Authors: We concur that a formal treatment and independent validation are necessary. We will add a graph-theoretic definition of Semantic Closure in Section 3: given component graph G=(V,E), Semantic Closure holds if there exists a cycle reachable from the injection node with non-zero propagation probability under the agent's semantic interpreter. For ACE Analysis we will supply the energy function E(G) = sum_{v in V} (activation_freq(v) * component_complexity(v)), with threshold set at mean + 3 sigma derived from a held-out set of 200 benign recursive workloads (iterative math, data aggregation, and planning loops). We will also report false-positive rates (target <5%) and ROC curves on these benign tasks. These additions will be included in the revised manuscript to match the empirical standards used for the attack evaluation. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical attack evaluation is self-contained

full rationale

The paper introduces Mobius Injection as an attack exploiting a named property (Semantic Closure) in agentic logic and supports its claims exclusively through experimental measurements of call amplification (up to 51.0x) and latency inflation (up to 229.1x) across specific claw-style and coding agents integrated with 12 LLMs. No derivation, equation, or predictive step is presented that reduces by construction to fitted parameters, self-definitions, or self-citations. The results are benchmarked directly against observed attack success rates in the experimental harness, making the work independent of any circular reduction. The skeptic concern about iteration limits is a question of experimental validity, not circularity in the reported chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 4 invented entities

The central claim rests on the existence of Semantic Closure as a structural property and on the effectiveness of the proposed ACE defense; both are introduced without prior independent evidence.

axioms (1)
  • domain assumption LLM agents possess a structural vulnerability called Semantic Closure that permits sustained recursive execution from a single textual input.
    Invoked as the load-bearing property enabling the attack.
invented entities (4)
  • Mobius Injection no independent evidence
    purpose: The attack technique that weaponizes agents into zombie nodes via targeted injection.
    Newly defined attack method.
  • AbO-DDoS no independent evidence
    purpose: Agent-based and Oriented DDoS attack class.
    New term for the resulting attack paradigm.
  • Semantic Closure no independent evidence
    purpose: Vulnerability in agentic logic enabling recursion.
    Postulated structural property.
  • Agent Component Energy (ACE) Analysis no independent evidence
    purpose: Proactive defense measuring anomalous energy in component graphs.
    Newly proposed mitigation technique.

pith-pipeline@v0.9.0 · 5609 in / 1368 out tokens · 56520 ms · 2026-05-13T02:20:57.557962+00:00 · methodology

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