DTap-Bench (Direct Attacks): leaderboard
Resistance of tool-using AI agents to misuse on DTap-Bench (Chen et al., 2026): the user is the attacker and pursues a harmful goal with optimized prompt, tool, skill and environment injections. Attack success rate in percent over 1,878 tasks in 14 domains, averaged by domain, where a rule-based check of the final environment state must find the malicious goal fully achieved; lower is better.
Metric: Attack success rate (%; 1,878 direct red-teaming agent tasks: the user is the attacker, with prompt, tool, skill and environment injections; an attack succeeds when a rule-based check of the final environment state finds the malicious goal fully achieved; fixed attack sequences that DTap-Red optimized on a surrogate agent; mean of the 14 domain rates). Source: arxiv.org. Saturation forecast: Not forecast. 8 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Sonnet 4.5 | 26.9 | #244 |
| 2 | GPT-5.5 | 28.9 | #48 |
| 3 | GPT-5.2 | 38.6 | #172 |
| 4 | GPT-OSS-120B | 46.5 | #699 |
| 5 | Gemini 3 Pro | 47.9 | #135 |
| 6 | GPT-5.4 | 51 | #121 |
| 7 | DeepSeek V4 Pro | 59.6 | #181 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=dtap-bench-direct-attacks · How It Works · Data refreshed daily, snapshot 2026-11-12.