The hacker-fixer loop reduces attack success rates on agent benchmark verifiers to 0% on KernelBench by alternating LLM hacker, fixer, and solver agents that discover and block exploits while preserving legitimate solutions.
Detecting Safety Violations Across Many Agent Traces
7 Pith papers cite this work. Polarity classification is still indexing.
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abstract
To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare, complex, and sometimes even adversarially hidden and only detectable when multiple traces are analyzed together. These challenges arise in diverse settings such as misuse campaigns, covert sabotage, reward hacking, and prompt injection. Existing approaches struggle here for several reasons. Per-trace judges miss failures that only become visible across traces, naive agentic auditing does not scale to large trace collections, and fixed monitors are brittle to unanticipated behaviors. We introduce Meerkat, which combines clustering with agentic search to uncover violations specified in natural language. Through structured search and adaptive investigation of promising regions, Meerkat finds sparse failures without relying on seed scenarios, fixed workflows, or exhaustive enumeration. Across misuse, misalignment, and task gaming settings, Meerkat significantly improves detection of safety violations over baseline monitors, discovers widespread developer cheating on a top agent benchmark, and finds nearly 4x more examples of reward hacking on CyBench than previous audits.
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2026 7roles
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BenchJack audits 10 AI agent benchmarks, synthesizes exploits achieving near-perfect scores without task completion, surfaces 219 flaws, and reduces hackable-task ratios to under 10% on four benchmarks via iterative patching.
CapCode constructs coding datasets with randomized tests that deliberately cap non-cheating performance below one, enabling detection of cheating via scores exceeding the cap, while CapReward reduces cheating in training.
Pilot study shows agent decision reconstructability varies by vendor SDK regime, with completeness scores from 42.9% to 85.7% and consistent gaps in reasoning traces.
A blind replay script matches frontier model performance on static CUA benchmarks due to non-principled environments and evaluation methods, prompting PRISM design principles and the DigiWorld benchmark with improved statistical aggregation.
AuditRepairBench supplies a large trace corpus and four screening methods that reduce evaluator-channel ranking instability in agent repair leaderboards by a mean of 62%.
The paper introduces KISS Sorcar, a simple open-source AI agent framework with a five-layer hierarchy and git worktree isolation to address context limits, error propagation, and reviewability in software engineering tasks.
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Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops
The hacker-fixer loop reduces attack success rates on agent benchmark verifiers to 0% on KernelBench by alternating LLM hacker, fixer, and solver agents that discover and block exploits while preserving legitimate solutions.