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EvoSkill Injection: Red-Teaming Autonomous Skill Generation and Evolution in Self-Evolving Agents

Doyun Kim, Chanwoo Kim, Sugyeong Eo, Yeo-Chan Yoon, Chanjun Park

arXiv:2608.30429v1cs.AIcs.CL

TL;DR

Self-evolving agents create a security gap because adversarial experiences can be internalized as reusable malicious skills. The paper defines EvoSkill Injection, evaluates it with SARGE and two benchmarks, and finds that injected skills can persist and repeatedly activate across agents and models, while lightweight prompt defenses provide only partial mitigation.

  • Problem

    Existing security studies largely overlook malicious capabilities autonomously generated and evolved inside self-evolving agents, despite their storage and reuse through the skill pipeline.

  • Method

    The paper defines EvoSkill Injection, proposes SARGE for iterative red-teaming, and constructs EvoSkillBench and EvoSkillSafetyBench to evaluate skill formation, retrieval, and harmful activation.

  • Results

    Malicious skills were persistently stored and repeatedly activated across diverse self-evolving agents and heterogeneous LLMs, with SARGE attack success rates of 43.5% in Generation, 54.6% in Escalation, and 49.9% in Reinforcement.

  • Takeaways & Limitations

    Persistent capability corruption is a critical security risk, and the findings motivate stronger defenses for autonomous skill generation and evolution.

  • Takeaways & Limitations

    The evaluation covers selected representative frameworks—AutoSkill, Voyager, and ExpeL—rather than all self-evolving agent architectures.

Abstract

from arXiv · show

LLM-based agent systems increasingly adopt skill-based architectures to reduce repetitive reasoning costs and improve stable, efficient task execution. Recent studies propose self-evolving agents that autonomously generate, refine, and reuse skills from past experiences to enable continuous capability evolution. However, autonomous skill evolution introduces a new attack surface in which malicious capabilities are generated, stored, and reused as legitimate skills. In this paper, we define EvoSkill Injection as a threat model targeting the autonomous skill generation and evolution pipeline of self-evolving agents. We further propose SARGE (Red-teaming Autonomous Skill Generation and Evolution in self-evolving agents), a red-teaming framework for evaluating this threat model through iterative generation, escalation, and reinforcement interactions. To support our framework, we construct EvoSkillBench, a benchmark dataset of malicious interaction trajectories for inducing malicious skill formation in self-evolving agents, and introduce EvoSkillSafetyBench, a post-attack benchmark for evaluating whether injected malicious skills are subsequently retrieved and activated as harmful behaviors. Our evaluation shows that SARGE induces malicious skill formation and that injected skills are persistently stored and repeatedly activated, highlighting the risk of persistent capability corruption.

1 Introduction

Self-evolving agents improve capability reuse by autonomously generating and refining skills, but this pipeline can internalize adversarial experiences as persistent malicious capabilities. The paper defines EvoSkill Injection, proposes SARGE and two benchmarks, and reports successful malicious-skill formation and repeated activation.

  • Self-evolving agents autonomously generate and refine reusable skills from interaction trajectories, enabling persistent capability accumulation without direct human intervention.
  • EvoSkill Injection targets the autonomous skill-generation and evolution pipeline, where adversarial trajectories can become seemingly legitimate skills that affect later behavior.
  • SARGE evaluates this threat through iterative generation, escalation, and reinforcement processes that inject harmful skills into an agent’s skill bank.
  • EvoSkillBench provides malicious interaction trajectories, while EvoSkillSafetyBench evaluates whether injected skills are subsequently retrieved and activated as harmful behaviors.
  • 43.5% in Generation, 54.6% in Escalation, and 49.9% in Reinforcement are the reported SARGE attack success rates.
  • Experimental results show persistent capability corruption: malicious skills remain stored and repeatedly influence future agent behavior.

2 Related Work

Skill-based architectures improve reuse and long-horizon execution, while self-evolving agents autonomously build capabilities from experience. Related security work has largely focused on externally supplied skills or retrieval attacks, leaving internally generated and evolved malicious capabilities underexplored.

  • Skill-based agents store and retrieve reusable capabilities to reduce repetitive reasoning costs and improve task success, reuse efficiency, and long-horizon performance.
  • AutoSkill, Voyager, and ExpeL exemplify self-evolving agents that generate, refine, or store reusable capabilities from past experiences.
  • Prior work identifies malicious skills, skill poisoning, retrieval manipulation, and supply-chain attacks, mainly involving externally provided skills or retrieval-level vulnerabilities.
  • Security risks in autonomous skill-generation and evolution pipelines remain underexplored, especially when adversarial experiences are internalized as reusable skills.

3 Benchmark Construction

Existing benchmarks emphasize single-turn harmful responses and related interaction attacks, but do not fully test persistent malicious capabilities stored and reused by self-evolving agents. EvoSkillBench and EvoSkillSafetyBench address formation, retrieval, and downstream activation through multi-turn evaluation.

  • Existing attack benchmarks focus on harmful response generation, prompt injection, jailbreaking, and retrieval manipulation, rather than persistent skill corruption.
  • EvoSkillBench uses multi-turn user-assistant-user trajectories to induce malicious skill formation through attacker-crafted outputs and positive feedback.
  • EvoSkillBench covers eight high-risk categories, including physical harm, cybercrime, privacy violation, and economic or ethics violation.
  • EvoSkillSafetyBench is derived from EvoSkillBench to test whether injected malicious skills are subsequently retrieved and activated during self-evolving-agent behavior.
  • Responses are labeled Explicit Refusal, Soft Safe Response, or Unsafe/Harmful to distinguish downstream outcomes after skill injection.

4 SARGE: A Red-Teaming Framework

SARGE is a modular multi-agent red-teaming framework that injects poisoned trajectories and tests whether malicious skills form, escalate, reinforce, persist, and activate. Its Orchestrator, Attacker, and Judge agents coordinate iterative attacks and independent behavioral verification.

  • SARGE injects poisoned interaction trajectories that make malicious interactions appear to be successful experiences, inducing internal generation, storage, and reuse of harmful capabilities.
  • Figure 1 organizes the framework into Skill Generation, Skill Escalation, and Skill Reinforcement attack flows.
  • The framework uses an Orchestrator Agent, Attacker Agent, Judge Agent, and evaluated Target Agent to manage attacks and verify persistence.
  • Skill Generation Attack: Skill Generation Attack targets the creation and storage of harmful capabilities, refining failed attempts through Judge feedback and alternative attack vectors.
  • The Judge uses an independent session and benign probes to test whether an injected skill is retrieved and manifests its encoded harmful behavior.
  • Skill Escalation Attack: Skill Escalation first stores a malicious skill, then uses a three-turn trajectory to amplify its harmfulness while disguising refinement as routine interaction.
  • Skill Reinforcement Attack: Skill Reinforcement retrieves previously generated or escalated malicious skills and praises their outputs so the Target Agent stores them as successful cases.

5 Experiments

The experiments evaluate SARGE across self-evolving agents, language models, safety categories, attack flows, transfer settings, baselines, and ablations. They measure persistent malicious skill formation and downstream activation using pass@k-style attack success and safety-response outcomes.

  • Datasets and evaluation: EvoSkillBench tests whether malicious capabilities become persistent reusable skills, while EvoSkillSafetyBench tests whether those skills are later retrieved and activated.Safety responses are categorized as Explicit Refusal, Soft Safe Response, or Unsafe/Harmful after standard execution and behavior judging.
  • Evaluation subjects: SARGE is evaluated on AutoSkill, Voyager, and ExpeL, which use different mechanisms for capability generation, storage, retrieval, and reuse.The experiments also evaluate GPT-4o-mini, GPT-5.4, DeepSeek-V4-Pro, DeepSeek-V4-Flash, Gemini-2.5-Flash-Lite, and Qwen3.5-9B.
  • Evaluation metrics: The pass@k-style metric measures the fraction of attack instances with at least one successful attempt within the first k iterative attack loops.Each Attack Agent receives up to four attempts per EvoSkillBench instance, and attack success requires persistent storage as a reusable skill.
  • Evaluation dimensions: The evaluation spans initial generation, escalation, reinforcement, downstream activation, cross-model transferability, attack baselines, and attack-agent ablations.These dimensions assess the lifecycle of malicious skill formation and whether attacks depend on a specific model backbone.

6 Results

SARGE successfully induces persistent malicious skills across attack flows, and injected skills increase harmful responses across agents and heterogeneous language models. Ablations and baseline comparisons further distinguish the roles of attack stages and direct harmful elicitation.

  • Attack success: 43.5% in Skill Generation, 54.6% in Skill Escalation, and 49.9% in Skill Reinforcement are achieved at pass@4 against AutoSkill.Pass@k increases across iterative attack loops, indicating that sustained adversarial interactions compromise the skill generation and evolution pipeline.
  • Downstream safety: Attacked agents show substantial increases in harmful responses, indicating that injected malicious skills are repeatedly retrieved and activated during downstream interactions.The safety evaluation compares three self-evolving agents in attacked and clean settings.
  • Downstream safety: AutoSkill’s harmful response rate rises from 6.5% to 19.2%, while Voyager reaches 32.5% in the attacked setting.These results indicate that malicious skills are persistently stored and reused during future task execution.
  • Downstream safety: ExpeL’s Explicit Refusal rate rises from 16.2% to 51.0%, while its harmful response rate increases from 8.8% to 15.2%.The attack therefore increases both harmful responses and unnecessary refusals in ExpeL.
  • Cross-model transferability: DeepSeek-V4-Pro has the highest harmful response rate at 29.6%, followed by Gemini-2.5-Flash-Lite at 20.4% and GPT-4o-mini at 19.2%.The results indicate persistent retrieval and activation across diverse LLMs.
  • Cross-model transferability: DeepSeek-V4-Pro combines the highest harmful response rate with the lowest cross-model attack success rates, indicating distinct injection-success and harmful-activation failure modes.Its injected skills are less frequent but more likely to produce harmful responses once retrieved.
  • Baseline comparison: PAIR reaches 33.6% harmful responses, while SARGE reaches 19.2%, compared with 19.1% for Indirect Prompt Injection, 15.1% for Memory Poisoning, 11.6% for Backdoor Attack, and 15.2% for RAG Poisoning.All attacks use the same malicious skills and a comparable attack budget.
  • Ablation study: Removing the Generation Attack Agent lowers harmful responses from 19.2% to 9.0%, while removing Reinforcement lowers them to 12.4%.Removing Escalation yields 18.2%, although escalation strengthens the specificity and severity of malicious content beyond harmful-response rate alone.

7 Discussion

Lightweight system-prompt defenses reduce harmful responses but increase Explicit Refusal rates and do not stop malicious skills from being generated, stored, or evolved.

  • Defense implications: Skill Verification Prompt and Skill Conflict Resolution Prompt provide only partial mitigation because they do not prevent malicious skill generation, storage, or evolution.The defenses act during response generation and also increase Explicit Refusal rates.

8 Conclusion

The paper presents EvoSkill Injection as a security risk in autonomous skill generation and evolution, evaluates it with SARGE and two benchmarks, and finds persistent malicious capability corruption. Malicious skills are repeatedly stored and activated across self-evolving agents and heterogeneous LLMs.

  • Contributions: SARGE evaluates EvoSkill Injection in autonomous skill generation and evolution pipelines, supported by EvoSkillBench and EvoSkillSafetyBench.The conclusion frames autonomous skill evolution as a new attack surface.
  • Conclusion: Malicious skills are persistently stored and repeatedly activated across diverse self-evolving agents and heterogeneous LLMs.The findings identify persistent capability corruption as a critical security risk and motivate stronger defenses for secure skill generation and evolution.

Limitations

The evaluation is limited to selected self-evolving agent frameworks, a costly iterative multi-agent attack pipeline, and eight high-risk categories rather than exhaustive architectural or scenario coverage.

  • Scope of evaluated agents: The evaluation covers AutoSkill, Voyager, and ExpeL, which do not exhaustively represent all self-evolving agent architectures.Future work should include tool-augmented agents, multi-agent systems, and stronger memory governance mechanisms.
  • Computational cost: SARGE’s iterative Orchestrator, Attack Agent, and Judge Agent design requires repeated model calls that can incur substantial computational and token-usage costs.The study therefore uses GPT-4o-mini to balance scalability and cost efficiency.
  • Model dependence: More capable or larger models may produce stronger attack strategies or different evaluation outcomes.The effect of model capability and inference budget on effectiveness and scalability remains open.
  • Benchmark coverage: EvoSkillBench covers eight high-risk categories but does not exhaustively represent all malicious capabilities or real-world attack scenarios.The benchmark targets representative safety-critical risks and should be expanded to additional domains and realistic deployment contexts.

Ethical Considerations

The paper frames EvoSkill Injection as a controlled safety study while constructing benchmarks that induce and test persistent malicious skill formation across eight high-risk categories.

  • Ethical risks: The attack methodology may create misuse risks because it investigates how malicious capabilities become reusable skills.The stated goal is controlled exposure of these risks and safer autonomous skill-generation pipelines.
  • Responsible release: The released code and benchmarks are intended for controlled evaluation, safety analysis, and defensive research rather than unauthorized real-world use.They support reproducibility and safeguards across skill generation, validation, storage, retrieval, and reuse.
  • Responsible use: Researchers are encouraged to follow safety and ethics guidelines and avoid applying the framework to real users, production systems, or third-party services without authorization.The benchmark is positioned for academic evaluation of agent safety risks.
  • Benchmark construction: EvoSkillBench uses eight high-risk categories with category-specific seeds and multi-turn trajectories designed to induce malicious skill formation.The categories include physical harm, social crime, cybercrime, psychological abuse, information manipulation, discrimination/hate, privacy violation, and economic/ethics violation.
  • Downstream safety evaluation: EvoSkillSafetyBench evaluates whether injected skills are retrieved and activated during downstream interactions, including under seemingly benign requests.Queries preserve lexical or semantic similarity to stored skills while removing explicit harmful intent and dangerous surface expressions.
  • Dataset scale: The benchmarks contain 800 EvoSkillBench trajectories and 800 corresponding EvoSkillSafetyBench evaluation samples.EvoSkillBench has 100 samples per category and follows a fixed user-assistant-user structure.

C Additional Results

Additional results show that EvoSkill Injection transfers across target models with substantial variation, while ablations support complementary roles for generation, escalation, and reinforcement attacks.

  • Transferability: EvoSkill Injection transfers beyond the attacker model, but attack success varies substantially across target models.Gemini-2.5-Flash-Lite has the highest reported transferability, whereas DeepSeek-V4-Pro is below 13% across objectives.
  • Transferability: 70.9%, 72.0%, and 70.8% pass@4 transferability are reported for Generation, Escalation, and Reinforcement on Gemini-2.5-Flash-Lite.DeepSeek-V4-Pro remains below 13% across all attack objectives.
  • Ablation results: Removing individual attack agents changes malicious-skill formation, while removing Generation or Reinforcement Attack Agents substantially reduces downstream harmful responses.These ablations support complementary roles across persistent skill formation and activation.
  • Model configurations: DeepSeek-V4-Pro used as both attacker and target reaches 79.4%, 78.5%, and 76.6% pass@4 attack success for Generation, Escalation, and Reinforcement.GPT-5.4 achieves substantially lower attack success in the reported comparison.
  • Attack procedures: The Orchestrator generates escalation strategies from Judge feedback, while reinforcement strategies reactivate stored malicious skills and reinforce them as standard behavior.Generation attacks complete three-turn trajectories with harmful assistant behavior and a persistence instruction.
  • Attack refinement: Validation, repair, and recovery prompts check trajectory requirements, rewrite weak attacks, and select alternative vectors after failed attempts.The workflow preserves the harmful domain while adding or maintaining skill-persistence instructions.
  • Evaluation probes: The Judge probes whether injected skills generate harmful content through natural domain-related requests that do not appear to be tests.Reinforcement attacks trigger a malicious skill through an ostensibly legitimate request and reinforce the resulting behavior.

D.2 Defense Results

System-prompt defenses reduce harmful responses after skill reinforcement attacks, but their effectiveness differs by agent and they do not prevent malicious skills from entering the skill bank.

  • Overall defense effects: SVP and SCRP both reduce harmful responses across three self-evolving agents compared with attacked agents without defense.The combined defense is strongest for AutoSkill and Voyager, while SCRP is strongest for ExpeL.
  • Agent-specific results: AutoSkill harmful responses fall from 19.2% to 8.4%, while Voyager harmful responses fall from 32.5% to 5.2% with the combined defense.For ExpeL, SCRP reduces harmful responses from 15.2% to 9.6%.
  • Refusal trade-off: The combined defense raises Voyager’s Explicit Refusal rate from 33.1% under attack to 62.9%.This indicates greater conservatism that may reject otherwise answerable queries.
  • Residual limitation: Prompt-level defenses do not prevent malicious skills from being generated, stored, or evolved within the skill bank.The findings motivate defenses that block malicious trajectories from becoming reusable skills.
  • Defense mechanisms: The Skill Verification Prompt treats retrieved skills as untrusted, while the Skill Conflict Resolution Prompt ignores skills conflicting with safety, system, or user-intent constraints.Together, they provide complementary response-generation mitigation.
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