Rohan Paul (@rohanpaul_ai) · 10/15/2026, 22:11:52
Google and CMU Propose Harmony Harness: Multi-Objective Evolution Boosts Agent Security Retention to 55%
Google and Carnegie Mellon University introduced Harmony Harness, a multi-objective evolution framework that prevents security degradation in self-improving agents during task migration. By optimizing test pass rates and exploit blocking as separate goals and distinguishing general fixes from benchmark-specific patches, the system achieved a 55% retention of security gains on unseen crash types in SecRepoBench, compared to 0% for traditional single-objective baselines.
SOURCE COVERAGEFull Original Report
Rohan Paul (@rohanpaul_ai) · 2026-10-15 14
New Google and Carnegie Mellon paper finds self-improving agents hold on to security gains far better when security is evolved as a separate goal.
Agent harnesses, the prompts, tools and loops around a model, can now rewrite themselves from feedback. Most such systems chase a single score, like test pass rate, and the security gains they pick up mostly vanish on new tasks.
Harmony Harness evolves several harness versions, each pushed toward a different balance of passing tests and blocking exploits. It also keeps general fixes separate from benchmark-specific ones, and picks which pieces to use per task.
On unseen crash types in SecRepoBench, 66.7% of its fixes both worked and blocked the exploit, against 36.7% for the starting harness. It kept 55% of its security gain on those tasks, while the DGM baseline kept 0%.
When you let an agent improve itself, track security as its own score and check it on held-out tasks.
