Case · 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.

Scope

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.

At a glance

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

Link to claim
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

Link to claim

Unknowns and contradictions

  • No universal effectiveness ranking for untested models, tasks or enterprise deployment patterns.

Sources and citation

Material revision history

  1. Oct 15, 2026 · Canonical change recorded · new in release · revision 150