Dynamic Risk Assessments for Offensive Cybersecurity Agents
Foundation models are increasingly becoming better autonomous programmers,
raising the prospect that they could also automate dangerous offensive
cyber-operations. Current frontier model audits probe the cybersecurity risks
of such agents, but most fail to account for the degrees of freedom available
to adversaries in the real world. In particular, with strong verifiers and
financial incentives, agents for offensive cybersecurity are amenable to
iterative improvement by would-be adversaries. We argue that assessments should
take into account an expanded threat model in the context of cybersecurity,
emphasizing the varying degrees of freedom that an adversary may possess in
stateful and non-stateful environments within a fixed compute budget. We show
that even with a relatively small compute budget (8 H100 GPU Hours in our
study), adversaries can improve an agent's cybersecurity capability on
InterCode CTF by more than 40\% relative to the baseline -- without any
external assistance. These results highlight the need to evaluate agents'
cybersecurity risk in a dynamic manner, painting a more representative picture
of risk.
68354f30d795fadab0623699
Boyi Wei
claimed_verified
2025-05-27T07:49:18.459Z
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Boyi Wei
user
boyiwei
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Benedikt Stroebl
admin_assigned
2025-05-27T12:43:02.035Z
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Benedikt Stroebl
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benediktstroebl
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Jiacen Xu
claimed_verified
2025-05-27T15:49:51.045Z
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Jiacen Xu
user
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Joie Zhang
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2025-05-27T12:43:39.808Z
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Joie Zhang
user
joie-zhang
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Zhou Li
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Peter Henderson
admin_assigned
2025-05-27T12:43:32.665Z
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Peter Henderson
user
breakend
Dynamic Risk Assessments for Offensive Cybersecurity Agents
Foundation models are increasingly becoming better autonomous programmers,
raising the prospect that they could also automate dangerous offensive
cyber-operations. Current frontier model audits probe the cybersecurity risks
of such agents, but most fail to account for the degrees of freedom available
to adversaries in the real world. In particular, with strong verifiers and
financial incentives, agents for offensive cybersecurity are amenable to
iterative improvement by would-be adversaries. We argue that assessments should
take into account an expanded threat model in the context of cybersecurity,
emphasizing the varying degrees of freedom that an adversary may possess in
stateful and non-stateful environments within a fixed compute budget. We show
that even with a relatively small compute budget (8 H100 GPU Hours in our
study), adversaries can improve an agent's cybersecurity capability on
InterCode CTF by more than 40\% relative to the baseline -- without any
external assistance. These results highlight the need to evaluate agents'
cybersecurity risk in a dynamic manner, painting a more representative picture
of risk.
Adversaries can significantly enhance foundation model capabilities in offensive cybersecurity with limited computational resources, underscoring the need for dynamic threat model assessments.
foundation models autonomous programmers offensive cybersecurity model audits cybersecurity risks verifiers financial incentives iterative improvement threat model stateful environments non-stateful environments compute budget InterCode CTF