Enabling an easy-path approval policy makes routine agent tasks smoother but also lowers friction for unauthorized effects. The authors tested specific harness configurations and attack surfaces under deterministic oracles. Some isolated mechanisms blocked attacks at substantial utility cost, while command allow/deny controls could change outcomes favorably…
Inspect the ClaimsCase · DiggingBeagle record
Auto-approve sharply increased attack effects in a coding-agent harness benchmark
HarnessSecurity-Bench reports unauthorized effects increased from 29.2% to 95.6% when auto-approval was enabled in controlled runs, while task utility also rose.
Controlled agent harness benchmark across specified models and tasks.
Assessment profile
Assessment method- Setting
- evaluation
- Exploitation
- demonstrated
- Evidence
- Grade B
- Remediation
- mitigation available
- AI role
- AGAINST AI
Basis and provenance
The published harness study reported deterministic unauthorized-effect outcomes in specified test environments. A configuration change is documented, not a measured production compromise or universal fix. Assessment uses the pinned Claims and source identities; public primary pages were not independently retrievable during this run. Impact remains unrated because field consequence or a comparable scoring rubric is not established.
Assessed Oct 15, 2026 using diggingbeagle.assessment/1.
Evidence basis: CLM-CP7-HARN-AUTO · CLM-CP7-HARN-SETUP · HarnessSecurity-Bench: Do Security Mechanisms Really Protect Coding Agent Harnesses?
At a glance
No universal effectiveness ranking for untested models, tasks or enterprise deployment patterns.
Limits and uncertaintyFull account
Enabling an easy-path approval policy makes routine agent tasks smoother but also lowers friction for unauthorized effects. The authors tested specific harness configurations and attack surfaces under deterministic oracles. Some isolated mechanisms blocked attacks at substantial utility cost, while command allow/deny controls could change outcomes favorably without prohibiting all work. The results are not rates observed in actual customers; they demonstrate why agent security must be evaluated at the enforcement mechanism and task level rather than inferred from a model’s refusal text.
Auto-approval changes the permission boundary rather than the language model weights. Replaying the same attack with approvals independently enforced would isolate whether the observed increase arises from action dispatch or changed model behavior.
Claims & evidence
CLM-CP7-HARN-AUTOUnder the GLM-5.2 baseline, auto-approval increased attack completion from 29.2% to 95.6%.supported
Basis: reported finding
- supportsHarnessSecurity-Bench: Do Security Mechanisms Really Protect Coding Agent Harnesses?primary disclosure
Abstract, security setting comparison
CLM-CP7-HARN-SETUPThe study evaluated nine security mechanisms across six coding-agent harnesses with 2,500 runs and deterministic oracles.supported
Basis: reported finding
- supportsHarnessSecurity-Bench: Do Security Mechanisms Really Protect Coding Agent Harnesses?primary disclosure
Abstract, experiment design and evaluation
Unknowns and contradictions
- No universal effectiveness ranking for untested models, tasks or enterprise deployment patterns.
Sources and citation
Material revision history
- Oct 15, 2026 · Canonical change recorded · new in release · revision 150