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REVIEW 3 major objections 5 minor 7 cited by

A single adversarial message can permanently hijack an LLM agent's core configuration and turn it into a self-propagating worm vector.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-02 18:05 UTC pith:QPRWDNGH

load-bearing objection First credible demonstration of a self-replicating worm in a production agent framework, with a genuine Vector B supply-chain gap that needs fixing before the strongest claims hold. the 3 major comments →

arxiv 2603.15727 v3 pith:QPRWDNGH submitted 2026-03-16 cs.CR cs.AIcs.LGcs.MAcs.SE

AgentWorm: Self-Propagating Attacks Across LLM Agent Ecosystems

Yihao Zhang , Zeming Wei , Xiaokun Luan , Chengcan Wu , Zhixin Zhang , Jiangrong Wu , Haolin Wu , Huanran Chen
show 2 more authors
Jun Sun Meng Sun
This is my paper
classification cs.CR cs.AIcs.LGcs.MAcs.SE
keywords self-replicating wormLLM agent securitydual-anchor persistenceflat context trustprompt injectionskill supply chaincommand-and-controlmulti-agent ecosystem
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper argues that a large class of LLM agent frameworks, where persistent Markdown configuration files are loaded unconditionally into the system prompt and all channel messages share one flat context, enables a worm that needs only a single message to infect a victim. The worm, ClawWorm, injects its payload into the core configuration in two places — the session startup section and a global interaction rule — so it survives session restarts and fires on every reboot. It then autonomously sends the payload to every peer it interacts with, achieving multi-hop propagation without further attacker action. In 1,800 controlled trials across four LLM backends, three delivery vectors, and three payload types, the aggregate success rate was 64.5%, with the skill-marketplace vector succeeding on all tested models. If true, any similarly structured agent ecosystem is exposed to self-replicating compromise, and execution-level filters alone are insufficient because infection can spread even when the payload never executes.

Core claim

ClawWorm is presented as the first self-replicating worm demonstrated against a production-scale autonomous agent framework. Starting from one adversarial message in a shared channel, the victim agent is persuaded to append the worm to its AGENTS.md configuration file using two anchors: a Session Startup section that executes at every session initialisation, and a global interaction rule that piggybacks the payload onto any reply or tool output visible in a shared channel. On restart the payload fires with the agent's full tool privileges, and thereafter the agent autonomously propagates the complete payload to newly encountered peers. The evaluation crosses three delivery vectors (attacker-

What carries the argument

The central mechanism is dual-anchor persistence in the agent's core configuration file, combined with a flat context trust model. Because the framework unconditionally loads every Markdown workspace file into the system prompt at session start and gives all tokens equal authority regardless of provenance, a message from an arbitrary channel participant can cause the agent to write attacker-chosen directives into its own highest-privilege configuration. The first anchor (Session Startup) guarantees execution at each reboot; the second (global interaction rule) guarantees propagation during routine interactions. Three delivery vectors — web injection, skill supply chain poisoning, and direct

Load-bearing premise

The measured success rate transfers from the isolated testbed, where messages are delivered via a single-turn CLI without human oversight and all agents use default configurations, to real production deployments with continuous mixed traffic, human observers, and potentially hardened configurations.

What would settle it

In a live, human-supervised deployment of default-configured OpenClaw instances, deliver a single adversarial message to each of 100 instances and check whether AGENTS.md is modified without any other attacker action; if zero modifications occur, the paper's central persistence claim does not survive outside the automated CLI testbed.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • A single message, not a software exploit, is sufficient to gain persistent, restart-proof control of a default-configured LLM agent.
  • Infected agents become active carriers that spread the worm to every peer they interact with, independent of the attacker, sustaining multi-hop infection chains.
  • Execution-level safety filters (e.g., blocking curl|bash) reduce payload execution but do not stop propagation; 'asymptomatic carriers' continue to spread the worm.
  • The skill-marketplace delivery vector evades safety reasoning across all four tested backends, implying that unaudited third-party skill distribution is a universal weak point.
  • Because the infected state is absorbing (no self-healing), an epidemiological model with R0 > 1 predicts saturated infection of a 40,000-instance ecosystem in a handful of interaction cycles.

Where Pith is reading between the lines

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

  • The dual-anchor pattern generalizes to any agent framework that loads unverified text configuration into the system prompt; a direct test would be to port the attack to another such framework and observe whether persistence and propagation survive.
  • The high success of the skill-marketplace vector suggests that community extension stores, not just prompts, are the most efficient worm distribution channel; this motivates static analysis and sandboxed execution as a stronger near-term defense than prompt filtering.
  • The asymptomatic-carrier result implies that monitoring agents for outbound propagation directives, such as messages containing configuration-instruction blocks, may catch infections that tool-execution logging misses.
  • If LLM providers introduce provenance-aware context, where tokens are tagged by source, the flat-context root cause could be mitigated at the model level, but the paper leaves this untested.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents ClawWorm, a self-replicating prompt-injection worm targeting the OpenClaw agent framework. The claimed attack cycle is: a single adversarial message induces the victim to write a dual-anchor payload into its AGENTS.md configuration; on every session restart the payload executes with the agent's full tool privileges; the infected agent then autonomously propagates the payload to new peers via one of three vectors (attacker-controlled URL, malicious ClawHub skill, or direct instruction replication). The authors report a 64.5% aggregate attack success rate across 1,800 trials spanning four LLM backends, three vectors, and three payloads, with multi-hop propagation up to 5 hops and 100% persistence across 5 restarts. They also propose four layered defenses and present an SI-model epidemiological projection.

Significance. If the claims hold, this is an important contribution to agent security: it demonstrates a concrete end-to-end worm lifecycle in a widely deployed agent framework, uses real-execution verification with ground-truth file artifacts, and highlights model-specific divergences in security posture. The factorial design and the cross-framework transferability experiment strengthen the claim that the vulnerability is architectural. However, the supply-chain vector's propagation step is not actually exercised, and the testbed's session model is underspecified, so the headline numbers should be read with caution.

major comments (3)
  1. [§4.1, Algorithm 1, Table 1, Table 5] The testbed's 'strict boundary' in §4.1 lists 'pre-installed skill packages' as a scripted component, but Algorithm 1 defines Vector B as the infected agent recommending a ClawHub skill, the target installing it, and setup directives then modifying AGENTS.md. The measured Vector B ASR of 0.81 (Table 1) and per-hop conditional ASR of 0.96 (Table 5) therefore only measure post-installation obedience to setup directives; they do not measure whether an infected agent can induce a peer to install the malicious skill. The conclusions 'skill supply chains remain universally vulnerable' and the R0 = k × ASR projection in §4.5 rest on this unmeasured precondition. Please either run the full installation step as an autonomous action or reframe Vector B as a post-installation skill-directive vector and remove the supply-chain propagation claims.
  2. [§4.1 vs §3.5.1] The setup states that messages are delivered via the OpenClaw CLI and 'each invocation constitutes a single-turn session,' while §3.5.1 and the retry analysis in Table 2 report up to eight conversational turns per attempt and up to three attempts per trial. These statements are in tension: if each CLI invocation is a separate single-turn session, the measured effect cannot be attributed to a multi-turn handshake. Please specify explicitly how turns and attempts were implemented (e.g., context carried across invocations, or a persistent session), and state whether the 64.5% aggregate ASR is after retries or at first message.
  3. [§4.3, Tables 1-3] No confidence intervals or significance tests are reported for the per-cell, per-vector, or per-model ASR values. With n=50 per cell, many differences may be within sampling noise. For example, the Vector A×P2 row (0.42 vs 0.88 across models) is likely significant, but adjacent cells such as Vector A×P1 (0.66 vs 0.70) are not. The claims about 'stark divergences' and the retry boost would be more robust with binomial CIs or a formal test. This is not a fatal flaw, but it is necessary for the comparative security-posture conclusions.
minor comments (5)
  1. [Abstract] The abstract first says '63% aggregate attack success rate' and later '64.5%'; §4.3 reports 64.5%. Please align the numbers.
  2. [Title/metadata] The arXiv metadata lists the paper as 'AgentWorm' while the full text uses 'ClawWorm'. Pick one name and use it consistently.
  3. [Table 3] The caption for Table 3 duplicates the caption of Table 1, but the table reports per-phase metrics. Update the caption.
  4. [§4.3] The statement that payload aggregates (P1: 0.71, P2: 0.64, P3: 0.59) 'confirm strict payload independence' is inaccurate given the spread; rephrase to 'no dominant payload effect' or similar.
  5. [References] Reference [1] has a broken URL with a space in 'github.com/OpenC law'; also 'V oyager' in the references should be 'Voyager'.

Circularity Check

0 steps flagged

No circularity: attack success rates are measured, and the epidemiological projection applies, rather than presupposes, those measurements.

full rationale

The paper's load-bearing results are empirical measurements, not derivations from definitions or self-citations. The aggregate 64.5% ASR (§4.3) comes from 1,800 controlled trials; persistence is verified by reading AGENTS.md, execution by real file artifacts after a session restart, and propagation by a fresh-channel probe (§4.2). The epidemiological projection (§4.5) is an explicit model application: R0 = k × ASR uses the measured ASR as an input, and the conclusion that any ASR > 0 eventually saturates an SI population follows from the stated absorbing-state assumption, so it is a model property rather than circular reasoning. Self-citations ([4], [39], [40]) appear only in related-work or defense-validity contexts and are not load-bearing for the central attack results. Two caveats were considered and are validity gaps, not circularity. First, Vector B's 'pre-installed skill packages' are scripted (§4.1), so the 81% Vector B ASR is a conditional measurement of setup-directive compliance after installation, not of the recommendation-and-installation step; the abstract's 'skill supply chains remain universally vulnerable' overstates this quantity, and Table 5's per-hop 0.96 carries the same condition. This is missing support, not circular reasoning, because the measured conditional ASR is not derived from the precondition—the precondition simply makes the measurement narrower than the claim. Second, the abstract's claimed cross-framework experiment on Hermes Agent is absent from the full text, an evidentiary omission. These issues affect external validity but do not reduce any claimed result to its own input by construction.

Axiom & Free-Parameter Ledger

4 free parameters · 6 axioms · 0 invented entities

The central empirical claim is a measured phenomenon, not a derivation, so the ledger is dominated by domain assumptions about OpenClaw's runtime and about the testbed's fidelity. The only fitted inputs are the measured ASR values used in the epidemiological projection; the main unvalidated bridge is treating pre-installed skill packages as equivalent to a real ClawHub supply-chain route.

free parameters (4)
  • Aggregate ASR (p) = 0.645
    Measured from 1,800 testbed trials; used as per-contact transmission probability in the R0 model (R0 = k × ASR, §4.5). It is a fitted/measured input to the projection, not an independently derived constant.
  • Average peer degree k = 5
    Chosen for the epidemiological projection (§4.5); not measured from OpenClaw deployments. The saturation conclusion depends only on ASR > 0, but the velocity claim depends on k.
  • Per-hop conditional ASR by vector = A=0.86, B=0.96, C=0.74
    Measured from 30 chains per vector (§4.4, Table 5); used to project multi-hop depth and attenuation.
  • Retry parameters K, T = K=3, T=8
    Experimental protocol choices (§4.1). The retry boost table (Table 2) depends on these; different values would change ASR.
axioms (6)
  • domain assumption OpenClaw loads all workspace files (SOUL.md, AGENTS.md, SKILL.md) into the system prompt unconditionally at every session start, with no integrity verification.
    §2.1 and §3.5.2. This is the persistence/execution enabler; the paper cites the framework release but does not independently verify beyond its testbed.
  • domain assumption The LLM assigns equal authority to tokens regardless of provenance (flat context trust), so channel messages can modify core configuration.
    §2.1 and §3.5.1. Required for single-message infection; empirically supported by ASR results but assumed as an architectural property.
  • domain assumption The controlled channel-relay testbed and CLI invocation model faithfully approximate production OpenClaw interactions.
    §4.1; weakened by §5.3 external threats, where the authors admit production traffic, human observers, and non-default configs would lower success.
  • domain assumption Vector B's pre-installed skill package is equivalent to a ClawHub supply-chain installation.
    §4.1 lists pre-installed skill packages as scripted; §3.2 claims attacker publishes to ClawHub. The equivalence is not tested, so this assumption is load-bearing for the supply-chain conclusion.
  • domain assumption Epidemiological SI model with absorbing infected state and no recovery; homogeneous network with average degree k.
    §4.5. The saturation conclusion follows from the model, not from field data.
  • domain assumption Tested LLM backends represent the security posture of production OpenClaw deployments.
    §4.1 selects four frontier backends from a leaderboard; Claude was excluded due to access restrictions.

pith-pipeline@v1.3.0-alltime-deepseek · 14779 in / 14952 out tokens · 140685 ms · 2026-08-02T18:05:17.215405+00:00 · methodology

0 comments
read the original abstract

Autonomous LLM-based agents increasingly operate as long-running processes forming densely interconnected multi-agent ecosystems, whose security properties remain largely unexplored. Systems such as OpenClaw, an open-source platform with over 40{,}000 active instances, persistent configurations, tool-execution privileges, and cross-platform messaging, are deployed at scale, yet the security of such agent ecosystems remains largely unexplored. This work presents AgentWorm, the first self-replicating worm attack against a production-scale agent framework, achieving a fully autonomous infection cycle initiated by a single message: the worm first hijacks the victim's core configuration to establish persistent presence across session restarts, then executes an arbitrary payload upon each reboot, and finally propagates itself to every newly encountered peer without further attacker intervention. The attack is evaluated on a controlled testbed across five distinct LLM backends, three infection vectors, and three payload types. Results show a 63\% aggregate attack success rate, sustained multi-hop propagation, and stark divergences in model security postures, highlighting that while execution-level filtering effectively mitigates dormant payloads, skill supply chains remain universally vulnerable. Defenses are evaluated at three layers (prompt-level mitigations sourced from real community practice, the framework's built-in security controls, and an ecosystem-wide measurement of public configurations), revealing that the critical controls capable of breaking the infection loop are not enabled in any of the observed deployments. A cross-framework transferability experiment on Hermes Agent confirms that the underlying vulnerabilities are properties of the autonomous agent design pattern, not artifacts of a single implementation.

Figures

Figures reproduced from arXiv: 2603.15727 by Chengcan Wu, Haolin Wu, Huanran Chen, Jiangrong Wu, Jun Sun, Meng Sun, Xiaokun Luan, Yihao Zhang, Zeming Wei, Zhixin Zhang.

Figure 1
Figure 1. Figure 1: An illustration of the ClawWorm infection lifecycle within the OpenClaw network. An [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overall illustration of the ClawWorm pipeline. The self-replication cycle comprises three [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: ClawWorm propagation characteristics across different OpenClaw ecosystem conditions. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

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Forward citations

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