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From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents

Adversarial context planted in a repository can steer coding agents into requesting dangerously broad allow-rules, and agents on production platforms including Claude Code and Codex CLI elicited the target rules in 58% of experimental instances, according to an arXiv paper (2610.22987v1). Once approved, the saved rules covered 56.9% of category-specific capability test cases, and conditional on approval the exploit achieved verified, silent goal completion in 47 of 58 eligible instances (81.0%), undermining human-in-the-loop oversight. The work targets the permission-update process in execution harnesses, which intercept out-of-sandbox actions and prompt users for approval to reduce approval fatigue.

by read1 min views1 publishedOct 19, 2026

arXiv:2610.22987v1 Announce Type: new Abstract: Coding agents use execution harnesses to constrain the consequences of prompt injection by enforcing permission boundaries. These systems deterministically intercept any out-of-sandbox action and prompt the user for approval. To reduce approval fatigue, users can approve allow-rules proposed by the agent, after which any future action matching the rule is allowed without a prompt. In this work, we show that this mechanism introduces a new risk: untrusted context can influence the permissions presented for approval, with consequences extending beyond the immediate task. We investigate privilege escalation targeting this permission-update process. Specifically, adversarial context planted in a repository can reliably steer an agent performing a routine task into requesting a plausibly justified, yet dangerously broad allow-rule. If the user approves the request, the saved rule can effectively blind the harness, enabling adversaries to execute malicious actions that would otherwise require approval, without triggering human oversight. Across production platforms, including Claude Code and Codex CLI, we demonstrate that agents successfully elicit the target allow-rules in 58% of experimental instances. Once acquired, these rules exhibit a massive semantic blast radius, covering 56.9% of category-specific capability test cases. To further quantify the downstream risk, we show that conditional on rule approval, our exploit achieves verified, silent goal completion in 47 of the 58 eligible instances (81.0%), undermining the safety promise of human-in-the-loop oversight.

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