Self-evolving agents increasingly convert interaction histories into reusable skills that persist beyond individual tasks. While prior work studies memory and retrieval poisoning, such attacks only affect agents when poisoned records are retrieved as context. We uncover a new and more fundamental risk: poisoned experiences can be transformed by the agent itself into durable behavioral artifacts. We present \textbf{SkillJack}, the first attack that exploits the experience-to-skill pipeline of self-evolving agents. Instead of directly manipulating runtime context, SkillJack hijacks the agent's own learning process to implant malicious behaviors into its reusable skill repertoire. We identify three key properties of this transformation: \emph{sanitization whitewashing}, where malicious intent is obscured during skill extraction; \emph{cross-layer promotion}, where transient experiences become persistent capabilities; and \emph{persistence isolation}, where the attack survives removal of its original source records. We evaluate SkillJack on two representative systems, SkillX and Anything2Skill, using a shared dataset of 150 trajectories across four policy-risk categories. Results show that skill extraction substantially reduces attack detectability: in SkillX, safety detection drops from 98.5\% for poisoned trajectories to 11.4\% for extracted skills, while Anything2Skill shows a similar effect. Meanwhile, the implanted skills remain effective, achieving attack success rates of 56.2\% and 89.2\% on the two systems, respectively. Furthermore, 80.0\% of skill-mediated attacks persist after deleting the original poisoned records, and some skills unintentionally activate on benign queries. Our findings reveal skill evolution as a new attack surface and motivate provenance-aware skill lifecycle protection. Our code is available at https://github.com/Tencent/AI-Infra-Guard/research/skilljack.
Zonghao Ying, Xiang-Fan Wu, Hui-Yu Wu et al.· 2 citations
We assess indirect prompt injection in DeepSeek Harness (DSH), using AI-Infra-Guard (A.I.G) to construct tests, deliver controlled taint, execute DSH, collect traces, and judge outcomes. The study covers 14,560 controlled executions over 16 indirect-content channels, text and file carrier modes, 35 payload objectives, one unmodified baseline, and 12 attack methods. The experiment preserves DSH's agent loop, tool registry, model adapter, and session-event path; source tools and sensitive sinks are local fixtures, so attempted actions are recorded without external side effects. We evaluate each trace with a deterministic rule-based judge, \JudgeR{} (RuleJudge), and a semantic LLM-based judge, \JudgeL{} (LLMJudge). The strongest observed attack success rates are 17.0% under \JudgeL{} for fake-completion attack in text mode, 25.5% under \JudgeR{} for hidden Unicode in file mode, and 16.0% under \JudgeR{} for the skills channel in file mode. \JudgeL{} also assigns partial compliance more often than \JudgeR{} (7.3% versus 2.0%). We relate these results to DSH's treatment of tool results, additional contexts, and tool-call policy hooks, then identify controls that should sit between untrusted content and sensitive actions. Our code is available at https://github.com/Tencent/AI-Infra-Guard/tree/main/Research/deepseek-harness-security-assessment .
Zonghao Ying, Xiangfan Wu, Huiyu Wu et al.· 0 citations
AFL and EFL have little detectable route-level association with GPQA-Diamond accuracy and pronounced EFL coincides with a decline in Terminal-Bench pass rate as task exposure increases, a pattern may arise because correctness in long-horizon tasks is more sensitive to extreme fidelity loss.
Xiangfan Wu, Zonghao Ying, Huiyu Wu et al.· 0 citations
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