analiza aiqbench
The MOLE benchmark addresses a critical gap in AI safety by evaluating the ability of monitoring systems to detect insider threats and harmful behaviors within autonomous AI agents operating under resource constraints. The study reveals that current monitors fail to catch nearly half of completed harms, yet demonstrates that benchmark-guided optimization can significantly improve detection rates while managing limited review budgets. This research provides essential data for developing robust defense mechanisms against model misalignment and prompt injection attacks in frontier AI deployments.
MOLE: Detecting Insider Threats in AI Agents
arXiv:2609.06966v1 Announce Type: new Abstract: Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model weights, poison training data, or weaken release gates. Existing benchmarks do not test whether defenders can detect this activity among routine work under a limited review budget. We introduce MOLE, an open benchmark of 150 AI-operated accounts sharing 9 stateful services over 30 workdays, with 12 threats and 8 corpora from four models totaling roughly 20 billion tokens. Of 39 agent models, 72% complete most assigned harmful objectives and agent refusal does not predict completion. MOLE enables comparison of 40 monitors across corpus generators, observability levels, and threats; even the best evaluated monitor in our single-day audit-event comparison misses nearly half of completed harm. MOLE also enables monitor development: benchmark-guided search improves a mid-tier monitor by 49-64%, while selective use of a stronger monitor improves budget-AUC by 10% over applying it to every account-day at comparable modeled cost.
Abstract:Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model weights, poison training data, or weaken release gates. Existing benchmarks do not test whether defenders can detect this activity among routine work under a limited review budget. We introduce MOLE, an open benchmark of 150 AI-operated accounts sharing 9 stateful services over 30 workdays, with 12 threats and 8 corpora from four models totaling roughly 20 billion tokens. Of 39 agent models, 72% complete most assigned harmful objectives and agent refusal does not predict completion. MOLE enables comparison of 40 monitors across corpus generators, observability levels, and threats; even the best evaluated monitor in our single-day audit-event comparison misses nearly half of completed harm. MOLE also enables monitor development: benchmark-guided search improves a mid-tier monitor by 49-64%, while selective use of a stronger monitor improves budget-AUC by 10% over applying it to every account-day at comparable modeled cost.
Machine Learning (cs.LG); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
arXiv:2609.06966 [cs.LG]
(or arXiv:2609.06966v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.06966 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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