New attack method bypasses LLM agent security checks by exploiting scope gaps

Researchers have developed a novel method called Scope-Gap Injection (SGI) to bypass security checks in multi-stage LLM agent safety pipelines. This technique exploits a discrepancy where the judging system (S2) reviews task instructions and skill definitions but not the content of attachments, while the agent later uses these attachments during execution (S3). By embedding malicious instructions within attachments, SGI successfully bypassed all three security stages for 5 out of 19 targets in the IJCAI 2026 OpenClaw Agent Security Attack Competition, achieving a top score of F = 0.385. The study also noted that placing attack instructions in attachments led to a higher acceptance rate by the S2 judge compared to in-skill placement. AI

IMPACT Introduces a new attack vector for LLM agents, highlighting potential vulnerabilities in current safety evaluation methods.

RANK_REASON Academic paper detailing a new method for attacking LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• S3
• F = 0.385
• IJCAI 2026
• LLM agent
• OpenClaw Agent Security Competition
• S2
• Scope-Gap Injection

• paper
• safety

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