LPS-Bench - Race-condition Exploitation: leaderboard

Metric: Success-conditioned safe rate (%): the share of agent trajectories that meet the case's own safety criterion at every planning and tool-calling step (a benign case is safe when completed with the needed safeguards or paused for clarification, an adversarial case when the agent refuses or halts before harm), among trajectories labeled safe or unsafe by a DeepSeek-R1 evaluator (execution failures excluded); on the 52 adversarial user-induced cases of risk type RC (timing gaps that leave the agent acting on stale state); LPS-Bench's 570 human-reviewed long-horizon tool-use cases with simulated MCP-style toolkits, each model in one LangChain agent at temperature 1 with up to 100 steps; higher is better. Source: arxiv.org. 13 models tracked.

Top models

#ModelScoreOverall rank
1Claude Sonnet 4.5100#138
2GPT-5.186.54#131
3Gemini 3 Pro69.23#77
4Claude Sonnet 446.15#194
5GPT-536.54#91
6Claude 3.5 Sonnet26.92#337
7Qwen 3 8B9.62#667
8Qwen 3 32B7.69#424
9DeepSeek V3.17.69#260
10Gemini 2.5 Pro5.77#145
11Llama 3.1 70B Instruct5.77#548
12DeepSeek V3.23.85#198
13Llama 3.1 8B Instruct1.92#1018

Interactive version: theaggregate.ai/benchmark?slug=lps-bench-race-condition-exploitation · How It Works · Data refreshed daily, snapshot 2026-10-11.