Capability-Based Scaling Laws for LLM Red-Teaming
As large language models grow in capability and agency, identifying
vulnerabilities through red-teaming becomes vital for safe deployment. However,
traditional prompt-engineering approaches may prove ineffective once
red-teaming turns into a weak-to-strong problem, where target models surpass
red-teamers in capabilities. To study this shift, we frame red-teaming through
the lens of the capability gap between attacker and target. We evaluate more
than 500 attacker-target pairs using LLM-based jailbreak attacks that mimic
human red-teamers across diverse families, sizes, and capability levels. Three
strong trends emerge: (i) more capable models are better attackers, (ii) attack
success drops sharply once the target's capability exceeds the attacker's, and
(iii) attack success rates correlate with high performance on social science
splits of the MMLU-Pro benchmark. From these trends, we derive a jailbreaking
scaling law that predicts attack success for a fixed target based on
attacker-target capability gap. These findings suggest that fixed-capability
attackers (e.g., humans) may become ineffective against future models,
increasingly capable open-source models amplify risks for existing systems, and
model providers must accurately measure and control models' persuasive and
manipulative abilities to limit their effectiveness as attackers.
68371955b0224460143a69bf
Alexander Panfilov
claimed_verified
2025-05-28T19:13:54.321Z
645410c50b50e97d3722f1d2
/avatars/e8e079981316b26dbf0f4ada6dcdc8c6.svg
Alexander Panfilov
user
kotekjedi
68371955b0224460143a69c0
Paul Kassianik
68371955b0224460143a69c1
Maksym Andriushchenko
68371955b0224460143a69c2
Jonas Geiping
claimed_verified
2025-06-27T08:58:10.559Z
63d86dbf3130cadcaf8bdd11
/avatars/29d79a0c6dcec01111ef192fecd0fa7a.svg
Jonas Geiping
user
JonasGeiping
Capability-Based Scaling Laws for LLM Red-Teaming
As large language models grow in capability and agency, identifying
vulnerabilities through red-teaming becomes vital for safe deployment. However,
traditional prompt-engineering approaches may prove ineffective once
red-teaming turns into a weak-to-strong problem, where target models surpass
red-teamers in capabilities. To study this shift, we frame red-teaming through
the lens of the capability gap between attacker and target. We evaluate more
than 500 attacker-target pairs using LLM-based jailbreak attacks that mimic
human red-teamers across diverse families, sizes, and capability levels. Three
strong trends emerge: (i) more capable models are better attackers, (ii) attack
success drops sharply once the target's capability exceeds the attacker's, and
(iii) attack success rates correlate with high performance on social science
splits of the MMLU-Pro benchmark. From these trends, we derive a jailbreaking
scaling law that predicts attack success for a fixed target based on
attacker-target capability gap. These findings suggest that fixed-capability
attackers (e.g., humans) may become ineffective against future models,
increasingly capable open-source models amplify risks for existing systems, and
model providers must accurately measure and control models' persuasive and
manipulative abilities to limit their effectiveness as attackers.
Red-teaming with large language models reveals that attack success drops sharply when the target model's capabilities exceed the attacker's, highlighting the need for new strategies to assess and mitigate future risks.
large language models red-teaming prompt-engineering LLM-based jailbreak attacks MMLU-Pro benchmark jailbreaking scaling law