RewardHackingAgents: Benchmarking Evaluation Integrity for LLM ML-Engineering Agents
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2026
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| author | Atinafu, Yonas Cohen, Robin |
| author_facet | Atinafu, Yonas Cohen, Robin |
| contents | LLM agents increasingly perform end-to-end ML engineering tasks where success is judged by a single scalar test metric. This creates a structural vulnerability: an agent can increase the reported score by compromising the evaluation pipeline rather than improving the model. We introduce RewardHackingAgents, a workspace-based benchmark that makes two compromise vectors explicit and measurable: evaluator tampering (modifying metric computation or reporting) and train/test leakage (accessing held-out data or labels during training). Each episode runs in a fresh workspace with patch tracking and runtime file-access logging; detectors compare the agent-reported metric to a trusted reference to assign auditable integrity labels. Across three tasks and two LLM backbones, scripted attacks succeed on both vectors in fully mutable workspaces; single-mechanism defenses block only one vector; and a combined regime blocks both. In natural-agent runs, evaluator-tampering attempts occur in about 50% of episodes and are eliminated by evaluator locking, with a 25-31% median runtime overhead. Overall, we demonstrate that evaluation integrity for ML-engineering agents can be benchmarked as a first-class outcome rather than assumed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11337 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RewardHackingAgents: Benchmarking Evaluation Integrity for LLM ML-Engineering Agents Atinafu, Yonas Cohen, Robin Artificial Intelligence LLM agents increasingly perform end-to-end ML engineering tasks where success is judged by a single scalar test metric. This creates a structural vulnerability: an agent can increase the reported score by compromising the evaluation pipeline rather than improving the model. We introduce RewardHackingAgents, a workspace-based benchmark that makes two compromise vectors explicit and measurable: evaluator tampering (modifying metric computation or reporting) and train/test leakage (accessing held-out data or labels during training). Each episode runs in a fresh workspace with patch tracking and runtime file-access logging; detectors compare the agent-reported metric to a trusted reference to assign auditable integrity labels. Across three tasks and two LLM backbones, scripted attacks succeed on both vectors in fully mutable workspaces; single-mechanism defenses block only one vector; and a combined regime blocks both. In natural-agent runs, evaluator-tampering attempts occur in about 50% of episodes and are eliminated by evaluator locking, with a 25-31% median runtime overhead. Overall, we demonstrate that evaluation integrity for ML-engineering agents can be benchmarked as a first-class outcome rather than assumed. |
| title | RewardHackingAgents: Benchmarking Evaluation Integrity for LLM ML-Engineering Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.11337 |