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ARXIV:2605.12673 · AI AGENT BENCHMARKING · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.12673AI AGENT BENCHMARKINGSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHHao Wang · Hanchen Li · Qiuyang Mang · Alvin Cheung · Koushik Sen · Dawn Song · arXiv
An automated red-teaming system that audits AI agent benchmarks to identify and patch reward-hacking exploits, ensuring more robust evaluations.
Opportunity summary
Pain An automated red-teaming system that audits AI agent benchmarks to identify and patch reward-hacking exploits, ensuring more robust evaluations.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
An automated red-teaming system that audits AI agent benchmarks to identify and patch reward-hacking exploits, ensuring more robust evaluations. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously…
Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Moreover, we extend BenchJack to an iterative generative-adversarial pipeline that discovers new flaws and patches them iteratively to improve benchmark robustness. Code availability is…
AI Agent Benchmarking moved forward this cycle; last verified May 2026. Public score 8.0/10. Production flags indicate code availability.
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An automated red-teaming system that audits AI agent benchmarks to identify and patch reward-hacking exploits, ensuring more robust evaluations.
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Paper Pack
10.48550/arXiv.2605.12673An automated red-teaming system that audits AI agent benchmarks to identify and patch reward-hacking exploits, ensuring more robust evaluations.
Abstract
Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in frontier models without overfitting. We argue that benchmarks must be secure by design. From past incidents of reward hacks, we derive a taxonomy of eight recurring flaw patterns and compile them into the Agent-Eval Checklist for benchmark designers. We condense the insights into BenchJack, an automated red-teaming system that drives coding agents to audit benchmarks and identify possible reward-hacking exploits in a clairvoyant manner. Moreover, we extend BenchJack to an iterative generative-adversarial pipeline that discovers new flaws and patches them iteratively to improve benchmark robustness. We apply BenchJack to 10 popular agent benchmarks spanning software engineering, web navigation, desktop computing, and terminal operations. BenchJack synthesizes reward-hacking exploits that achieve near-perfect scores on most of the benchmarks without solving a single task, surfacing 219 distinct flaws across the eight classes. Moreover, BenchJack's extended pipeline reduces the hackable-task ratio from near 100% to under 10% on four benchmarks without fatal design flaws, fully patching WebArena and OSWorld within three iterations. Our results show that evaluation pipelines have not internalized an adversarial mindset, and that proactive auditing could help close the security gap for the fast-paced benchmarking space.
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Proof status
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What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 8.0
PROBLEM
An automated red-teaming system that audits AI agent benchmarks to identify and patch reward-hacking exploits, ensuring more robust evaluations. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in frontier models...
METHOD
Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in frontier models without overfit...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Moreover, we extend BenchJack to an iterative generative-adversarial pipeline that discovers new flaws and patches them iteratively to improve benchmark robustness. Code availability is flagged in the pro...
WHY NOW
AI Agent Benchmarking moved forward this cycle; last verified May 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
An automated red-teaming system that audits AI agent benchmarks to identify and patch reward-hacking exploits, ensuring more robust evaluations. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in frontier models without overfitting.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in frontier models without overfitting.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Moreover, we extend BenchJack to an iterative generative-adversarial pipeline that discovers new flaws and patches them iteratively to improve benchmark robustness. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Agent Benchmarking moved forward this cycle; last verified May 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
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An automated red-teaming system that audits AI agent benchmarks to identify and patch reward-hacking exploits, ensuring more robust evaluations.
Segment
AI Agent Benchmarking
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
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Build Passport
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reason
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proof status
unverified
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next verification path
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Build readiness
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passport absent
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Artifact maturity
GitHub and Hugging Face maturity payloads
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fresh
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
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Buyer urgency is not verified from source.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Regulatory load
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Gaps
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Classify regulatory flags before commercialization planning.
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People
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Build Passport does not name an implementer.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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WATCHTOWER
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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