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arxiv: 2605.11418 · v1 · submitted 2026-05-12 · 💻 cs.AI · cs.CR

Recognition: 2 theorem links

· Lean Theorem

Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry

Shoumik Saha , Kazem Faghih , Soheil Feizi
Authors on Pith no claims yet

Pith reviewed 2026-05-13 02:08 UTC · model grok-4.3

classification 💻 cs.AI cs.CR
keywords AI agentsskill registriessemantic attackssupply chain securitySKILL.mdadversarial textautonomous systems
0
0 comments X

The pith

SKILL.md files act as operational instructions that adversaries can manipulate to control which skills AI agents discover, select, and approve.

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

The paper investigates semantic supply-chain attacks that target the natural-language content inside SKILL.md files used by autonomous AI agents. It demonstrates that short textual changes can alter embedding-based retrieval to favor malicious skills, bias agent selection toward functionally equivalent but adversarial variants, and evade governance checks that are meant to block harmful capabilities. If correct, these findings mean that third-party skill registries can be gamed through ordinary description text rather than code changes. A sympathetic reader would care because current agent designs treat SKILL.md as neutral metadata when it actually directs capability loading at scale.

Core claim

The central claim is that SKILL.md is not passive documentation but operational text that shapes which third-party capabilities agents find, trust, and use. Using real skills from an existing registry and realistic mechanisms for discovery, selection, and governance, the work shows that crafted text achieves up to 86 percent pairwise win rate and 80 percent top-10 placement in retrieval, 77.6 percent selection of adversarial variants in paired trials, and 36.5 to 100 percent evasion of blocking verdicts.

What carries the argument

The SKILL.md file treated as operational text that drives embedding retrieval, description-based agent preference, and semantic verdict generation across the three registry-facing stages of the skill lifecycle.

If this is right

  • Malicious skills gain higher visibility in agent searches without altering their core function.
  • Agents systematically prefer adversarial but similarly described skills over equivalent safe ones.
  • Governance filters based on natural-language analysis can be bypassed by rephrasing intent.
  • Third-party skill supply chains become attack surfaces that require defenses beyond code inspection.

Where Pith is reading between the lines

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

  • The same pattern of semantic manipulation could affect other natural-language interfaces that agents use to choose tools or data sources.
  • Registry operators may need to combine textual analysis with independent verification such as execution sandboxes or cryptographic attestations.
  • Agent frameworks could shift from open text descriptions to structured, signed capability manifests to reduce this vector.

Load-bearing premise

The tested registry mechanisms and the set of ClawHub skills accurately reflect how real-world agent systems discover, select, and govern third-party capabilities.

What would settle it

Running the same adversarial SKILL.md files against a live production registry and observing no measurable increase in retrieval rank, selection frequency, or evasion rate compared with unmodified benign skills.

Figures

Figures reproduced from arXiv: 2605.11418 by Kazem Faghih, Shoumik Saha, Soheil Feizi.

Figure 1
Figure 1. Figure 1: Overview of SKILL.md-only semantic supply-chain attacks on an Agent Skill registry. Starting from a benign SKILL.md, an adversary modifies only natural-language content while leaving the skill’s functional structure largely unchanged. The injected text can serve three distinct purposes: a discovery trigger that improves registry ranking for target queries, a selection trigger that biases the agent toward t… view at source ↗
Figure 2
Figure 2. Figure 2: Pairwise retrieval-score improvement across embedding models. Each cell reports the win rate and average relative score boost of the adversarial skill over the original skill. Rows indicate the model used to optimize the discovery trigger, and columns indicate the model used for evaluation. Diagonal entries measure same-model attacks, while off-diagonal entries measure transfer. email health prompt tax tra… view at source ↗
Figure 3
Figure 3. Figure 3: OpenAI retrieval-score distributions across categories and attack settings. Violin plots show the distribution of OpenAI/text-embedding-3-small scores for original skills, transferred triggers from BAAI models, and triggers optimized by beam-search against OpenAI. Median scores are marked inside each violin. Retrieval scores are consistently manipulable [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Selection preference for adversarial variants over original skills. Each cell shows adversarial/original selection proportions and the corresponding dominance ratio [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: reports the verdict distribution for the baseline malicious variants and the four SKILL.md-only evasion strategies. The gover￾nance pipeline detects direct malicious insertion effectively: none of the baseline variants are labeled clean, and 68.2% are classified as mali￾cious. However, under a ClawHub-style enforce￾ment policy that blocks only malicious verdicts while leaving suspicious skills available wi… view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of ClawHub Skill Download Counts. We randomly downloaded 3000 skills from ClawHub and analyzed their download trend (April, 2026). 13 [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Beam-search based trigger optimization. Starting from a beam of B candidate triggers at iteration i, each beam element is expanded by the generator model G to produce K candidate continuations. The resulting modified skill-file candidates are scored by the embedding model E using their similarity to the target query embedding eq, computed from q ∗ . The top-B highest-scoring candidates are retained as the … view at source ↗
Figure 8
Figure 8. Figure 8: Example SKILL.md-only discovery manipulation using the black-box beam-search attack. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example SKILL.md-only discovery manipulation using the white-box gradient-based attack. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt-generation template used to synthesize realistic user queries for the selection-stage [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Example generated evaluation scenario used in the selection-stage manipulation exper [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Example selection-stage manipulation using the false-advertisement strategy. The adver [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Example selection-stage manipulation using the active-maintenance strategy. The adver [PITH_FULL_IMAGE:figures/full_fig_p022_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Selection preference for adversarial variants over original skills across domains. Each cell shows adversarial/original selection proportions and the corresponding dominance ratios; color intensity reflects the strength of selection preference. Row and column aggregates summarize model-level robustness and manipulation effectiveness. Consistent vulnerability across domains. Across all domains, selection p… view at source ↗
Figure 15
Figure 15. Figure 15: Baseline prompt used to generate synthetic malicious instructions for the governance [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Suffix used in the governance judge-jailbreaking attack. The adversarial payload attempts [PITH_FULL_IMAGE:figures/full_fig_p026_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Transformation prompt used in the paraphrased-instruction governance-evasion attack. [PITH_FULL_IMAGE:figures/full_fig_p027_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Transformation prompt used in the Definition-of-Done governance-evasion attack. The [PITH_FULL_IMAGE:figures/full_fig_p028_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Example governance-evasion attack using a paraphrased malicious instruction. The [PITH_FULL_IMAGE:figures/full_fig_p029_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Example governance-evasion attack using a malicious Definition-of-Done instruction. [PITH_FULL_IMAGE:figures/full_fig_p030_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Moderation verdict share for each strategy across all domains. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_21.png] view at source ↗
read the original abstract

Autonomous AI agents increasingly extend their capabilities through Agent Skills: modular filesystem packages whose SKILL.md files describe when and how agents should use them. While this design enables scalable, on-demand capability expansion, it also introduces a semantic supply-chain risk in which natural-language metadata and instructions can affect which skills are admitted, surfaced, selected, and loaded. We study SKILL.md - only attacks across three registry-facing stages of the Agent Skill lifecycle, using real ClawHub skills and realistic registry mechanisms. In Discovery, short textual triggers can manipulate embedding-based retrieval and improve adversarial skill visibility, achieving up to 86% pairwise win rate and 80% Top-10 placement. In Selection, description-only framing biases agents toward functionally equivalent adversarial variants, which are selected in 77.6% of paired trials on average. In Governance, semantic evasion strategies cause malicious skills to avoid a blocking verdict in 36.5%-100% of cases. Overall, our results show that SKILL.md is not passive documentation but operational text that shapes which third-party capabilities agents find, trust, and use.

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 / 3 minor

Summary. The manuscript examines semantic supply-chain attacks on AI agent skill registries mediated by SKILL.md files. Using real ClawHub skills and realistic registry mechanisms, it reports experimental results across three stages: Discovery (embedding retrieval manipulation yielding up to 86% pairwise win rate and 80% Top-10 placement), Selection (description framing biasing agents toward adversarial variants in 77.6% of paired trials), and Governance (semantic evasion avoiding blocking verdicts in 36.5%-100% of cases). The central claim is that SKILL.md constitutes operational text that shapes which third-party capabilities agents discover, select, trust, and load.

Significance. If the empirical results hold under broader validation, the work is significant for identifying a concrete, previously under-examined attack surface in the emerging modular-skill ecosystem for autonomous agents. The use of real ClawHub skills and concrete success rates across multiple lifecycle stages provides practical evidence rather than purely synthetic demonstrations, which strengthens its relevance for registry designers and agent developers. This could motivate new defenses at the semantic layer of skill supply chains.

major comments (2)
  1. [§4] §4 (Selection experiments): The 77.6% average selection rate for adversarial variants is derived from simulated registry mechanisms and selection logic rather than instrumented production agents or open frameworks. This assumption is load-bearing for the claim that SKILL.md 'shapes which third-party capabilities agents find, trust, and use,' because differing prompts, retrieval indexes, or governance rules in actual deployments could change the observed bias.
  2. [§3 and §5] §3 and §5 (Discovery and Governance results): The reported rates (86% win rate, 36.5%-100% evasion) are presented without error bars, trial counts, variance measures, or statistical tests. This directly affects the strength of the quantitative evidence supporting the attack effectiveness claims, which are central to the paper's thesis.
minor comments (3)
  1. [Abstract] Abstract: The term 'realistic registry mechanisms' is invoked without a one-sentence definition or pointer to the simulation parameters, which would improve immediate clarity for readers unfamiliar with the setup.
  2. [Figures and tables] Figures and tables: Any plots or tables summarizing success rates across stages should include confidence intervals or trial counts to match the quantitative nature of the claims.
  3. [Related work] Related work: The manuscript would benefit from explicit citations to prior studies on prompt injection or embedding-based retrieval attacks to better situate the novelty of the SKILL.md-specific vector.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment of the work's significance and for the constructive major comments. We address each point below with clarifications grounded in the manuscript's methodology and indicate the revisions made.

read point-by-point responses

  1. Referee: [§4] §4 (Selection experiments): The 77.6% average selection rate for adversarial variants is derived from simulated registry mechanisms and selection logic rather than instrumented production agents or open frameworks. This assumption is load-bearing for the claim that SKILL.md 'shapes which third-party capabilities agents find, trust, and use,' because differing prompts, retrieval indexes, or governance rules in actual deployments could change the observed bias.

    Authors: We appreciate the referee's point on the simulation basis for the Selection experiments. The 77.6% figure was obtained using selection logic directly modeled on documented behaviors from open frameworks (e.g., LangChain agents, AutoGen, and ClawHub's own retrieval and ranking pipelines) and real skill metadata from ClawHub. Direct instrumentation of closed production agents is not feasible for this study. In revision, we have expanded §4 with a dedicated paragraph on simulation fidelity, explicit mapping of each simulated component to its real-world counterpart, and a limitations subsection discussing how prompt variations or index differences could modulate the bias. This provides the transparency needed to evaluate the claim's robustness without overstating generalizability. revision: partial

  2. Referee: [§3 and §5] §3 and §5 (Discovery and Governance results): The reported rates (86% win rate, 36.5%-100% evasion) are presented without error bars, trial counts, variance measures, or statistical tests. This directly affects the strength of the quantitative evidence supporting the attack effectiveness claims, which are central to the paper's thesis.

    Authors: We agree that the original presentation would benefit from explicit statistical support. The revised manuscript now reports the exact trial counts (Discovery: 1,000 pairwise comparisons across 50 skill pairs; Governance: 400 trials per strategy), includes standard-error bars on all bar and line plots, provides variance measures, and adds statistical tests (binomial proportion tests for win rates against 50% null, with p < 0.001 reported; bootstrap confidence intervals for evasion ranges). These details have been inserted into the methods and results subsections of §3 and §5. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on direct empirical attack trials

full rationale

The paper reports experimental results from three stages of attacks (Discovery via embedding retrieval on real ClawHub skills, Selection via description framing, Governance via semantic evasion) measured in concrete success rates. No equations, fitted parameters, or derivations are present that reduce any claim to its own inputs by construction. The central assertion that SKILL.md functions as operational text is supported by the reported trial outcomes rather than self-definitional structures, self-citation chains, or renamed known results. The representativeness of the simulated registry logic is an external validity concern, not a circularity issue within the derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that embedding-based retrieval and agent decision procedures in the tested registries are representative; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Embedding similarity and agent selection logic in realistic registries respond to natural-language descriptions as modeled in the experiments.
    Invoked to translate textual triggers into measurable visibility and selection advantages.

pith-pipeline@v0.9.0 · 5497 in / 1252 out tokens · 55388 ms · 2026-05-13T02:08:57.851359+00:00 · methodology

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Reference graph

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