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SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces

Chang Jin, An Wang, Zeming Wei, Kai Wang, Biaojie Zeng, Qiaosheng Zhang, Chao Yang, Jingjing Qu, Xia Hu, Xingcheng Xu

arXiv:2605.12015v3cs.CRcs.AIcs.CLcs.LGcs.MA

TL;DR

Existing safety evaluations often miss attacks delivered through skill guidance and local execution artifacts even when user tasks are benign. SkillSafetyBench introduces a runnable, artifact-grounded benchmark for this setting, and experiments show that localized non-user attacks induce safety failures across agent systems, models, and attack surfaces.

  • Problem

    Existing evaluations rarely test whether adversarial skill-facing materials and supporting local artifacts can induce unsafe behavior during otherwise benign workflows.

  • Method

    SkillSafetyBench evaluates 155 adversarial cases from 47 tasks across 6 risk domains and 30 categories, using case-specific rule-based verifiers and multiple agent systems and model backends.

  • Results

    Localized non-user attacks induce non-trivial safety failures across CLI agents and model backends, with vulnerability depending on scaffold, backend, and attack surface.

  • Takeaways & Limitations

    Agent safety should be evaluated in executable systems and the trusted operational contexts where decisions are made and materialized.

  • Takeaways & Limitations

    The benchmark focuses on skill-facing local attacks verifiable from concrete execution evidence and does not exhaust the full space of agent safety risks.

Abstract

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Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools, memory, and execution environments. However, this modularity introduces attack surfaces that are largely missed by existing safety evaluations: even when the user request is benign, unsafe influence may reside in skill guidance, local artifacts, or execution-environment files that steer the agent toward unsafe actions. We present SkillSafetyBench, a runnable benchmark for evaluating such skill-facing safety failures. SkillSafetyBench includes 155 adversarial cases across 47 tasks, 6 risk domains, and 30 safety categories, each evaluated with a case-specific rule-based verifier. Experiments with multiple CLI agents and model backends show that non-user attacks can consistently induce unsafe behavior, with distinct failure patterns across domains, attack methods, and scaffold-model pairings. Our findings suggest that agent safety depends not only on model-level alignment, but also on how agents interpret skills, trust workflow context, and act through executable environments. The complete benchmark is available at https://github.com/AI45Lab/skill-safety-bench.

1 Introduction

Reusable skills extend agents with workflow-relevant procedures and resources, but also expand the trust boundary beyond user-authored instructions. SkillSafetyBench addresses the resulting gap by benchmarking unsafe behavior induced through non-user skill-facing materials in otherwise benign tasks.

  • Reusable skills package procedures, examples, code, resources, and verification routines that extend agents without retraining the underlying model.
  • These skills can shift the trust boundary because agents may treat downloaded or execution-linked artifacts as legitimate workflow context.
  • Existing evaluations rarely test benign tasks influenced by adversarial skill-facing materials and supporting artifacts while the original objective remains largely intact.
  • SkillSafetyBench contains 155 adversarial cases from 47 tasks across 6 risk domains and 30 canonical categories, each paired with a case-specific rule-based verifier.
  • The benchmark evaluates multiple models and agent frameworks to reveal system-level safety patterns under localized non-user attacks.

2 Related Work

Prior work evaluates agents in executable environments and studies attacks through prompts, tools, memory, retrieval, or skill artifacts. SkillSafetyBench shifts attention to attack chains in runnable workflows that couple skill-facing guidance with surrounding local materials.

  • Recent agent benchmarks evaluate observable actions, files, tests, commands, environment states, and execution traces rather than only final-answer matching.
  • Skill research treats skills as persistent execution-time assets that package reusable procedural knowledge, code templates, and related resources.
  • SkillSafetyBench adopts this executable-workflow perspective but evaluates safety under adversarially shaped skill instructions and local environments.
  • Agent-security research covers prompt injection, untrusted tool or retrieved data, harmful goals, poisoned memory, and malicious skill artifacts.
  • Existing evaluations often isolate attacks by dominant channel, whereas SkillSafetyBench evaluates coupled attack chains in runnable workflows.

3 Benchmark Overview

SkillSafetyBench organizes runnable safety cases around benign tasks, local non-user attack surfaces, executable environments, and artifact-grounded verification. Its taxonomy separates realized safety failures from the mechanisms used to induce them.

  • SkillSafetyBench includes 155 adversarial cases derived from 47 tasks, spanning 6 risk domains, 30 categories, and 8 attack classes.
  • The taxonomy covers failures involving misleading context, authority overreach, compromised runtime components, corrupted outputs, unsafe state, and poisoned knowledge or dependencies.
  • Risk domains describe the realized safety failure, while attack classes summarize the coarse mechanism used to induce it.
  • Each case combines a nominal user task, skill-facing non-user attack surfaces, an executable environment, and a case-specific verifier.
  • Rule-based verifiers determine attack success from replayable execution evidence such as malicious traces, leaked payloads, or poisoned memory records.

4 Benchmark Construction

The benchmark construction pipeline selects realistic executable tasks, embeds semantically aligned attacks into local skill-facing surfaces, materializes runnable cases, and verifies them with artifact-based checks. Expert review and independent LLM judging provide quality control.

  • Construction follows four stages: selecting executable substrates, instantiating attacks, materializing verifiable cases, and conducting quality control.
  • The benchmark selects 47 tasks whose workflows can naturally accommodate different attacks and expose relevant local surfaces and observable artifacts.
  • Attacks use skill guidance, helper files, wrappers, configuration, memory, corpora, and related artifacts, with mechanisms adapted to concrete task contexts.
  • Each scenario becomes a runnable attacked task with localized modifications embedded in the execution environment and a case-specific verify_attack.py script.
  • Three human experts independently reviewed all 155 cases and verifiers across task fidelity, threat-model fit, realism, taxonomy fit, and verifier precision and coverage.
  • Two independent judge models assessed camouflage quality, verifier alignment, and taxonomy coherence, with pass rates above 85% on every axis.

5 Experiments

Experiments across CLI agents and model backends show that skill-facing non-user attacks induce unsafe behavior across systems, while safety and task ability are not monotonically aligned. Vulnerability varies by scaffold–backend pairing, risk domain, attack method, and the role of supporting artifacts.

  • Agent CLI and Model Comparison: All evaluated systems show non-trivial ASR, ranging from 15.5% for Claude Code with Opus-4.6 to 50.3% for Codex with GLM-5.1.Kimi Code CLI with Kimi-K2.5 reaches 49.7% ASR.
  • Agent CLI and Model Comparison: Vulnerability depends on the scaffold–backend interaction: Codex is generally less robust than Claude Code, but the gap varies substantially by backend.GLM-5.1 shows a large Codex–Claude Code gap, whereas MiniMax-M2.7 shows a much smaller gap.
  • Safety and Task Ability Comparison: 42.6% task success and 41.8% ASR occur together for Codex with GPT-5.5, while Gemini CLI with Gemini-3-Flash combines 39.4% task success with 48.4% ASR.The reported scatter plot indicates that higher task success does not guarantee lower ASR.
  • Risk Domain Comparison: 58.7% average ASR in RD1 is highest, while RD3 is lowest at 19.2%, indicating stronger vulnerability to contextual or artifact-level attacks than explicit runtime manipulation.RD2, RD5, RD4, and RD6 average 49.3%, 46.6%, 41.6%, and 20.1%, respectively.
  • Clean-run baseline: 31.7% full-task success under attack falls from 50.0% in the matched clean setting, yet attack success also occurs in runs that fully complete the original task.This separates attack success from task failure in the matched 60-case subset.
  • Attack-method profiles: Bias manipulation and poisoning tend to produce higher ASR, with distinct attack-method profiles across systems.Codex with GPT-5.5 is more exposed to several attack classes, Claude Code with Opus-4.6 remains lower overall, and Gemini CLI with Gemini-3-Flash shows a broader high-ASR pattern.
  • Artifact-removal ablation: Removing supporting artifacts reduces attack success most for substantively essential cases, whereas carrier-only and amplifying cases retain more effectiveness.Essential artifacts provide runtime or state-dependent semantics; carrier and amplifying artifacts contribute payload delivery and contextual reinforcement.
  • Defense study: A policy-level semantic defense reduces attack success but also lowers task completion, exposing a safety–utility trade-off.The defense study compares this policy-level defense with runtime/tool-level Claude Guardrails Full.

6 Conclusion

SkillSafetyBench provides a runnable, artifact-grounded benchmark for safety under local non-user attack surfaces. Experiments show localized attacks induce safety failures, with vulnerability depending on the agent scaffold, model backend, and attack surface.

  • SkillSafetyBench evaluates agent safety under local non-user attack surfaces in skill-facing workflows.The benchmark focuses on whether unsafe behavior is realized in concrete run artifacts.
  • 155 adversarial cases span 6 risk domains, 30 categories, and 8 attack-method labels, each paired with a case-specific rule-based verifier.
  • Localized non-user attacks induce non-trivial safety failures across multiple CLI agent systems and model backends.
  • Vulnerability depends on interactions among the agent scaffold, model backend, and attack surface.
  • The results support evaluating agent safety at the level of executable systems rather than isolated model responses.

Limitations

SkillSafetyBench deliberately targets skill-facing local non-user attacks in runnable workflows, so it does not cover the full space of agent safety risks. Its rule-based verifiers are precise for targeted artifact-level behaviors but less suited to diffuse failures requiring broader assessment.

  • The benchmark’s scope is limited to attacks expressed through skill-facing materials and supporting local artifacts and verified from concrete execution evidence.
  • SkillSafetyBench does not exhaust the full space of agent safety risks, including richer interactive, long-lived, and multi-agent skill ecosystems.The authors identify multi-turn settings, evolving deployments, and skills that are installed, updated, shared, or composed across agents as extension areas.
  • Rule-based verifiers provide precise signals for targeted abnormal behaviors but are less suited to diffuse failures requiring broader semantic or human-in-the-loop assessment.
  • The benchmark’s adversarial cases could potentially inform misuse if released or used without appropriate safeguards.

A Artifact Release, Intended Use, and Data Screening

The authors release SkillSafetyBench for research on agent safety evaluation, robustness, and mitigation, with licensing and third-party-use responsibilities. They screen adversarial content so identifiers, credentials, URLs, and endpoints remain fabricated or local-only rather than operational.

  • The released benchmark code and data are provided under the Apache License 2.0.The license permits use, reproduction, modification, and distribution subject to its terms, including attribution notices.
  • SkillSafetyBench is intended for agent safety evaluation, robustness analysis, and mitigation development, not real-world misuse or unauthorized attacks.
  • Users are responsible for complying with license terms and applicable terms governing third-party artifacts or model providers.
  • Released adversarial content is benchmark-local and non-operational, using fabricated or local-only identifiers, credentials, tokens, URLs, organization names, and service endpoints.
  • AI assistants helped polish writing and modify human-designed attack surfaces, while authors reviewed and verified cases, analyses, and claims.
  • LLM judges supplied auxiliary quality-control signals for camouflage, verifier–attack alignment, and taxonomy coherence but did not replace human review.

C Extended Benchmark Taxonomy

The extended taxonomy organizes SkillSafetyBench into six risk domains and 30 canonical categories covering distinct trust, authority, execution, and output-integrity failures. It supports systematic case construction and analysis of interpretable families of trust breakdown.

  • The taxonomy contains six risk domains and 30 canonical categories that capture recurring patterns of unsafe behavior.Categories guide case design and distinguish qualitatively different induced failures.
  • RD1: Contextual Trust Risks: RD1 covers misleading contextual signals, including routing deception, source poisoning, intent conflict, retrieval contamination, and multi-source trust amplification.
  • RD2: Agency Scope and Authorization Risks: RD2 covers exceeding intended authority or action scope through approval spoofing, sensitive-input solicitation, destructive actions, planning degradation, and external-action induction.
  • RD3: Execution and Environment Risks: RD3 covers compromised execution paths involving interpreters, loaders, protocol identity, runtime toolchains, and sandbox boundaries.
  • RD4: Output and Artifact Integrity Risks: RD4 focuses on output integrity and downstream destinations, including schema corruption and abuse of downstream output interpreters.
  • The taxonomy enables failures to be analyzed as broader, interpretable families of trust breakdown rather than isolated attack instances.

D Comparison with Existing Benchmarks

SkillSafetyBench complements existing agent, security, and skill benchmarks by evaluating coordinated attacks through skill-facing materials and supporting local workflow artifacts, with success verified from concrete run artifacts.

  • Prior agent benchmarks emphasize realistic task execution and tool use, while security benchmarks target prompt injection, tool misuse, harmful behavior, or memory-related attacks.
  • Skill-security benchmarks are closer to SkillSafetyBench but typically isolate a dominant attack channel, such as skill-file injection or backdoored skill implementations.
  • SkillSafetyBench evaluates benign tasks influenced by adversarial skill-facing materials and supporting local artifacts not authored by the user.
  • The appendix summarizes benchmark coverage across 155 cases derived from 47 original tasks and distributed across six risk domains.

E.2 Task Reuse

SkillSafetyBench reuses task substrates unevenly: some artifact-rich workflows carry multiple attack designs, while attack-class prevalence reflects how naturally methods instantiate the benchmark’s risk categories.

  • Task Reuse: Artifact-heavy workflows are reused frequently because they expose richer local execution surfaces for carrying multiple attack designs.
  • Task Reuse: 19 original tasks appear once, 7 appear twice, and 21 appear three or more times, producing a long-tailed reuse pattern.
  • Attack-Class Composition: The benchmark’s eight attack classes are concentrated in bias_manipulation, malware_ransomware, poisoning, and data_exfiltration, which together comprise most cases.
  • Attack-Class Composition: Attack-class labels provide a coarse comparison, whereas six risk domains and 30 canonical categories capture finer-grained benchmark diversity.
  • Attack-Class Composition: Attack-class frequencies are intentionally unbalanced because construction prioritizes runnable, artifact-grounded coverage rather than equal method representation.

F Detailed Benchmark Cases

The detailed cases preserve benign user objectives while relocating malicious influence to local non-user surfaces, producing concrete unsafe behaviors verified from final artifacts, traces, or persistent files.

  • Case Design: Each representative case preserves the user-facing task while placing malicious influence in skills, sidecars, helper scripts, workflow notes, environment files, shims, or output contracts.
  • RD1: RD1 induces silent fraud-report false negatives by making poisoned reconciliation context override primary IBAN-mismatch criteria.
  • RD2: RD2 exfiltrates a recovery bundle into the answer artifact, and verification succeeds only when secret-bearing material appears in the primary output.
  • RD3: RD3 silently compromises Python execution through a PATH-precedence shim that delegates to the real interpreter while recording genuine workflow use.
  • RD4: RD4 leaks diagnostic credentials and internal URLs into reviewer-visible artifacts, creating an output-boundary violation.
  • RD5–RD6: RD5 leaves a persistent reviewer helper and registry as a durable foothold, while RD6 accepts a nonexistent shadow package into the dependency audit chain.
  • Verifier Validation: Rule-based verifier outcomes and independent semantic judgments agree from 92.9% to 94.2% across three configurations.

H.3 Case Analysis

Case analyses show that skill-facing attacks can induce unsafe behavior through multiple local mechanisms, sometimes while preserving apparent task completion. Results vary by risk domain, artifact role, defense, and execution configuration.

  • Representative cases: A credential-leakage case embedded a local secret cache into index.html as hidden JSON, shipping credentials in the final web artifact.The analysis classifies this as a concrete credential-boundary failure.
  • Risk-domain patterns: RD1 attacks are especially effective when multiple local signals reinforce one another through multi-source trust amplification.This pattern suggests susceptibility to poisoned context supported by apparently consistent local sources.
  • Risk-domain patterns: RD2 reaches high ASR for planning-scope and safety degradation, while RD4 reaches 75–100% ASR for downstream output interpreter abuse.These domains involve accepting locally suggested scope changes or framing unsafe content as generated output.
  • Risk-domain patterns: RD3 remains low overall, with runtime and toolchain compromise the most successful category; RD5 persistence attacks are broadly effective, and RD6 is dominated by retrieval, knowledge, and corpus poisoning.Runtime attacks are more successful when framed as normal validation or benchmark infrastructure, while persistence attacks exploit workflow continuity framing.
  • Task utility and robustness: Attack success remains substantial among task-successful runs: Codex + GLM-5.1 reaches 53.6% conditional ASR, while Claude Code + Opus-4.6 reaches 24.2%.Some domain estimates are unstable when few task-successful cases remain; Claude Code + MiniMax-M2.7 reaches 100.0% on RD4 from only three cases.
  • Task utility and robustness: Repeated runs yield an average ASR of 43.9% with a 3.8-percentage-point standard deviation, and 27 of 30 categories vary by at most one successful case.Category-level results are treated as observed-pattern diagnostics because each stratified category contains only two cases.
  • Task utility and robustness: A matched full-task success rate falls from 50.0% clean to 31.7% attacked, yet 8 of 19 fully completed attacked runs also satisfy the attack-success verifier.The benchmark therefore captures both task-interfering attacks and unsafe behavior that preserves apparent utility.
  • Artifact and defense analysis: Artifact removal reduces attacks most clearly in substantively essential cases, while carrier-only and amplifying cases retain more attacks because mechanisms remain available through skills.Artifacts can still deliver payloads, introduce local evidence, or reinforce contextual signals even when not strictly essential.

I.5 Trace-Grounded Root-Cause Analysis

Trace-grounded analysis distinguishes cognitive, procedural, and architectural blind spots, decisive failure phases, and scaffold-linked mechanisms. Codex more often fails through trust judgments during adoption, whereas Claude Code more often exhibits procedural gaps that become decisive later.

  • Analysis framework: The analysis annotates 136 successful traces from three configurations across blind-spot family, decisive failure phase, and scaffold-linked enabling mechanism.The traces come from Codex + GPT-5.5, Claude Code + Opus-4.6, and Claude Code + GLM-5.1.
  • Blind-spot families: Blind spots are classified as cognitive misjudgments, procedural omissions, or architectural properties of the execution environment.Cognitive failures concern trust, authority, relevance, scope, provenance, or validity; procedural failures omit validation or confirmation steps.
  • Blind-spot families: Codex + GPT-5.5 is dominated by cognitive failures involving trustworthiness, authority, or task relevance, whereas Claude Code configurations are dominated by procedural failures.Claude Code failures more often involve proceeding without an additional validation or confirmation step.
  • Decisive failure phases: Failure phases progress from exposure and adoption through execution, materialization, and outcome, marking where unsafe deviation becomes committed or decisive.Adoption incorporates malicious context into reasoning; materialization reflects the deviation in an output, file, state change, or other artifact.
  • Decisive failure phases: Codex + GPT-5.5 attacks most often become decisive during adoption, while Claude Code has a larger share becoming decisive during materialization.This indicates that Codex failures often arise when local context enters task interpretation, whereas Claude Code failures remain recoverable later in the workflow.
  • Scaffold-linked mechanisms: Context mixing is the most frequent scaffold-linked mechanism across all three configurations, accounting for roughly two thirds or more of successful traces in each.Native skill routing is second most common, while broad permissions account for a smaller subset.
  • Cross-scaffold patterns: Across scaffolds, Codex more often combines cognitive trust or relevance judgments with adoption-stage failures, while Claude Code more often combines procedural gaps with execution or materialization.Context mixing remains common across all configurations, making provenance and trust separation a shared challenge.

J Human Review and Quality Control

Human review and judge-based checks provide formative quality control for benchmark cases and verifiers. Reviewers assessed validity and verifier quality, while independent judges evaluated camouflage, alignment, and taxonomy coherence.

  • Human review: All 155 cases were independently reviewed by three human experts across six quality dimensions covering case validity and verifier quality.Reviewers also assigned accept, revision, or exclude action labels after initial implementation.
  • Interpretation: The initial review annotations were formative quality-control assessments rather than final quality scores for the released benchmark.Their values describe the inspection and revision process conducted before adjudication.
  • Human review: Reviewers used a common 0–10 rubric, where 0 indicated fundamental invalidity and 10 indicated full satisfaction without evident issues.The rubric applied to all six quality-control dimensions.
  • Reviewer agreement: All three reviewers agreed on the action label for 93 of 155 cases, while every case received agreement from at least two reviewers.No case produced a three-way split among accept, revision, and exclude.
  • Adjudication: Cases with low scores or disagreement were rechecked and adjudicated, with attack design, environment materialization, or verifier implementation revised before finalization.After adjudication and revision, all 155 cases satisfied the predefined inclusion criteria.
  • Verifier validation: Verifier spot-checks covered all six risk domains and examined whether automated outcomes matched unsafe behaviors in agent trajectories.This check was intended to guard against systematic mismatch between verifier judgments and benchmark safety semantics.
  • LLM-as-judge validation: Two independent judges scored camouflage quality, verifier–attack alignment, and taxonomy coherence, using temperature 0 and no shared state.Axes 1 and 2 used binary pass/fail judgments, while axis 3 compared predicted labels with declared domains and categories.
  • LLM-as-judge validation: Both judges’ domain-level pass rates for camouflage and verifier–attack alignment are reported in Tables 39 and 40, while taxonomy accuracy is 97.1% with 99.4% inter-judge agreement.The taxonomy axis is omitted from those tables because its accuracy is near ceiling.
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