arxiv: 2604.09378 · v1 · submitted 2026-04-10 · 💻 cs.CR · cs.AI

Recognition: unknown

BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning

Guiyao Tie , Jiawen Shi , Pan Zhou , Lichao Sun

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:05 UTC · model grok-4.3

classification 💻 cs.CR cs.AI

keywords backdoor attacksagent skillsmodel poisoningsupply chain securityAI agentsmachine learning securitythird-party software

0 comments

The pith

A third-party agent skill can embed a poisoned model that activates hidden malicious behavior only when its parameters match attacker-chosen semantic combinations.

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

Agent systems allow third-party skills that sometimes include their own trained models for decision-making or classification. The paper shows that an attacker can release a skill whose model has been fine-tuned so it outputs a concealed payload precisely when routine parameters satisfy specific trigger rules, while answering normal queries correctly. The training mixes standard accuracy loss with targeted poisoning steps to keep the backdoor hidden. Tests across eight models from five families, using hundreds of queries in a controlled simulation of skill installation and execution, reach attack success rates as high as 99.5 percent on the triggered tasks and stay effective even when only 3 percent of the training data is poisoned. This establishes model-bearing skills as a supply-chain attack surface distinct from prompt-based exploits.

Core claim

BadSkill poisons the model inside a published skill so the model outputs a hidden payload exactly when skill parameters satisfy attacker-defined semantic trigger combinations. The embedded classifier is trained with a composite objective that includes classification loss, margin-based separation, and poison-focused optimization. In an environment that reproduces third-party skill installation, execution, and parameter handling, the attack produces up to 99.5 percent average success on eight triggered skills across eight architectures while preserving high accuracy on 571 negative-class queries, and remains effective at a 3 percent poison rate and under several text perturbations.

What carries the argument

The backdoor-fine-tuned embedded classifier inside the skill that activates only on chosen semantic trigger combinations in the skill parameters.

If this is right

Model-bearing skills create a supply-chain risk that cannot be addressed by prompt injection defenses alone.
A 3 percent poison rate already produces over 90 percent attack success, so small amounts of tainted data suffice.
The attack works across model sizes from hundreds of millions to billions of parameters and survives common text perturbations.
Third-party skills therefore require provenance checks and behavioral testing of any bundled models before installation.

Where Pith is reading between the lines

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

If many agent platforms adopt model-in-skill bundles, attackers could target high-traffic skills to reach large numbers of users with a single upload.
Runtime monitoring that flags unusual output patterns on parameter inputs could serve as a practical countermeasure beyond static vetting.
The same poisoning approach might transfer to skills that bundle other model types, such as generators or planners, if their inputs contain similar semantic structure.

Load-bearing premise

The simulation environment used for testing accurately reflects how real agent platforms install, execute, and handle parameters from third-party skills.

What would settle it

Releasing a BadSkill-poisoned skill in a live agent platform and measuring whether the hidden payload executes on trigger-parameter queries without triggering normal detection mechanisms.

Figures

Figures reproduced from arXiv: 2604.09378 by Guiyao Tie, Jiawen Shi, Lichao Sun, Pan Zhou.

**Figure 1.** Figure 1: Model-in-skill backdoor setting. A benign-looking skill behaves normally on clean inputs but activates hidden behavior under triggeraligned parameters. Motivated by this threat model, we present BADSKILL, a framework for studying model-in-skill backdoors in agent skill ecosystems. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: Workflow of BADSKILL. Stage I constructs trigger-aware training data and optimizes an embedded classifier over structured skill parameters. Stage II packages the trained classifier into the skill artifact and uses it for benign-or-payload routing at runtime. argument tuple satisfies a hidden conjunction over multiple fields. This distinction is central in skill ecosystems, where structured arguments are ex… view at source ↗

**Figure 3.** Figure 3: Trigger-complexity comparison across Qwen2.5-0.5B, 1.5B, and 3B. The three settings [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Poison-rate sweep across eight model architectures. Row 1 shows the Qwen2.5 family [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

read the original abstract

Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their execution logic. This creates a supply-chain risk that is not captured by prompt injection or ordinary plugin misuse: a third-party skill may appear benign while concealing malicious behavior inside its bundled model. We present BadSkill, a backdoor attack formulation that targets this model-in-skill threat surface. In BadSkill, an adversary publishes a seemingly benign skill whose embedded model is backdoor-fine-tuned to activate a hidden payload only when routine skill parameters satisfy attacker-chosen semantic trigger combinations. To realize this attack, we train the embedded classifier with a composite objective that combines classification loss, margin-based separation, and poison-focused optimization, and evaluate it in an OpenClaw-inspired simulation environment that preserves third-party skill installation and execution while enabling controlled multi-model study. Our benchmark spans 13 skills, including 8 triggered tasks and 5 non-trigger control skills, with a combined main evaluation set of 571 negative-class queries and 396 trigger-aligned queries. Across eight architectures (494M--7.1B parameters) from five model families, BadSkill achieves up to 99.5\% average attack success rate (ASR) across the eight triggered skills while maintaining strong benign-side accuracy on negative-class queries. In poison-rate sweeps on the standard test split, a 3\% poison rate already yields 91.7\% ASR. The attack remains effective across the evaluated model scales and under five text perturbation types. These findings identify model-bearing skills as a distinct model supply-chain risk in agent ecosystems and motivate stronger provenance verification and behavioral vetting for third-party skill artifacts.

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.

Desk Editor's Note private letter to a colleague

BadSkill shows a backdoor that poisons the model inside an installable agent skill and triggers on semantic parameter combos, with high ASR in their simulator at low poison rates.

read the letter

The core point is that this work targets a supply-chain angle specific to agent skills that bundle their own models. An attacker ships what looks like a normal skill, but the embedded classifier has been fine-tuned so that certain routine parameter patterns activate a hidden payload. They report up to 99.5% average attack success rate across eight triggered skills, and even a 3% poison rate gets them to 91.7% ASR. The numbers hold across eight architectures from five families and survive five kinds of text perturbation while keeping normal accuracy on clean queries.

Referee Report

1 major / 2 minor

Summary. The paper introduces BadSkill, a backdoor attack on agent skills that bundle learned model artifacts. An adversary publishes a seemingly benign skill whose embedded model is fine-tuned with a composite objective (classification loss + margin separation + poison optimization) so that it activates a hidden payload only when routine skill parameters satisfy attacker-chosen semantic trigger combinations. The attack is evaluated in an OpenClaw-inspired simulation environment across 13 skills (8 triggered, 5 controls), 571 negative-class and 396 trigger queries, and eight architectures (494M–7.1B parameters) from five families. Reported results include up to 99.5% average ASR on triggered skills at 3% poison rate while preserving benign accuracy, with additional sweeps and robustness tests under text perturbations.

Significance. If the simulation faithfully reproduces real third-party skill installation, execution, and parameter semantics, the work identifies a distinct model supply-chain risk in agent ecosystems that is not covered by prompt-injection or plugin-misuse defenses. The concrete ASR numbers, poison-rate sweeps, multi-scale evaluation, and perturbation robustness constitute reproducible empirical evidence under the stated conditions and could motivate provenance and behavioral-vetting requirements for model-bearing skills.

major comments (1)

[Evaluation / Simulation Environment] Evaluation / Simulation Environment (abstract and §4): The headline claims (99.5% ASR at 3% poison rate across eight models) rest entirely on results obtained inside the OpenClaw-inspired simulator. The manuscript asserts that this environment “preserves third-party skill installation and execution” and “parameter-passing semantics,” yet provides no fidelity audit, cross-platform replication on actual agent frameworks, or comparison of how parameters are wrapped/sanitized in real deployments. Because the trigger activation path depends on these semantics, any mismatch would invalidate transfer of the reported ASR numbers to practical settings.

minor comments (2)

[Abstract] Abstract: states “strong benign-side accuracy” and “remains effective … under five text perturbation types” but supplies neither the exact benign accuracy figures nor the perturbation types or statistical significance tests.
[Method] The composite training objective is described at a high level; a precise equation or pseudocode for the combined loss (classification + margin + poison term) would improve reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the simulation environment and its implications for the attack's practical relevance. We address the major comment below and have revised the manuscript to improve clarity on the evaluation setup and its limitations.

read point-by-point responses

Referee: [Evaluation / Simulation Environment] Evaluation / Simulation Environment (abstract and §4): The headline claims (99.5% ASR at 3% poison rate across eight models) rest entirely on results obtained inside the OpenClaw-inspired simulator. The manuscript asserts that this environment “preserves third-party skill installation and execution” and “parameter-passing semantics,” yet provides no fidelity audit, cross-platform replication on actual agent frameworks, or comparison of how parameters are wrapped/sanitized in real deployments. Because the trigger activation path depends on these semantics, any mismatch would invalidate transfer of the reported ASR numbers to practical settings.

Authors: We agree that the evaluation is conducted entirely within the OpenClaw-inspired simulator and that the transferability of the reported ASR depends on how faithfully the simulation captures real parameter-passing semantics. The original manuscript described the environment as preserving installation, execution, and parameter semantics but did not include an explicit fidelity audit or side-by-side comparisons with live agent frameworks. In the revised manuscript we have expanded Section 4 with: (1) a detailed description of how the simulator implements parameter passing and sanitization based on OpenClaw's documented interfaces, (2) justification that the semantic trigger combinations are designed to be invariant to common low-level wrapping variations, and (3) a new limitations subsection that explicitly states the ASR results apply under the simulated conditions and that empirical validation on production agent platforms remains future work. These changes make the scope of the claims transparent without overstating generalizability. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical ASR measured directly from simulation runs

full rationale

The paper describes an empirical backdoor attack formulation and reports measured attack success rates (ASR) and benign accuracy on held-out query sets inside an OpenClaw-inspired simulator. No equations, uniqueness theorems, or first-principles derivations are presented that reduce the reported ASR values to fitted parameters or self-citations by construction. The composite training objective is described at a high level without algebraic reduction to the target metric, and the 3% poison-rate result is obtained from direct experimental sweeps rather than statistical forcing. The evaluation therefore remains externally falsifiable and does not match any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on standard supervised fine-tuning assumptions plus the realism of the simulation environment; no new physical or mathematical entities are postulated.

free parameters (1)

poison rate
Fraction of training data that is poisoned; swept from low values up to 3% in reported results.

axioms (1)

domain assumption The simulation environment preserves third-party skill installation and execution semantics
Invoked to justify the benchmark validity.

invented entities (1)

BadSkill attack formulation no independent evidence
purpose: Composite training objective combining classification loss, margin separation, and poison-focused optimization
The method is introduced by the paper to realize the backdoor.

pith-pipeline@v0.9.0 · 5613 in / 1190 out tokens · 56587 ms · 2026-05-10T17:05:37.299701+00:00 · methodology

discussion (0)

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry
cs.AI 2026-05 unverdicted novelty 8.0

Semantic manipulations of SKILL.md descriptions enable effective supply-chain attacks that bias AI agent skill registries toward adversarial skills in discovery, selection, and governance.
AgentTrap: Measuring Runtime Trust Failures in Third-Party Agent Skills
cs.CR 2026-05 conditional novelty 6.0

AgentTrap shows that current LLM agents typically complete user tasks while silently accepting unsafe side effects from malicious third-party skills rather than refusing them.
SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces
cs.CR 2026-05 unverdicted novelty 6.0

SkillSafetyBench shows that localized non-user attacks via skills and artifacts can consistently induce unsafe agent behavior across domains and model backends, independent of user intent.

Reference graph

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