DeepSeek v4.1 Flash Is Now Our Best Hacking Model
summary
DeepSeek V4.1 Flash achieved a perfect 11/11 score on a hacking benchmark by gaining code execution on all vulnerable targets while keeping fixed targets secure. The model demonstrated a high level of competence by identifying both intended vulnerabilities and alternative attack paths within the test environment. This result highlights the need for benchmarks to evaluate both the final outcome and the specific attack path taken. The entire process was remarkably cost-effective, costing only $4.65 for the accepted runs.
key points
- The model successfully executed attacks on Grafana, Jenkins, and Nextcloud by reading source code, testing hypotheses, and adapting its approach in real-time.
- A detailed audit revealed that while six attacks followed the planned path, five used unexpected routes, proving the model's ability to find the fastest working solution regardless of the author's intent.
- The benchmark designers updated the test environment to close alternative routes and implement stricter path-level checks to ensure models are solving the specific intended weaknesses.
who should read it
Security researchers, AI developers, and benchmark designers who are interested in the model's hacking capabilities and the more precise measurement of AI agent performance.
discussion positive
consensus
Commenters are divided on theComparing DeepSeek v4.1 Flash and GLM 5.3, with some praising DeepSeek's extreme cost-efficiency and agentic performance while others find it prone to looping or less capable in complex security audits. There is a general agreement that results vary wildly based on the inference provider, quantization (FP8 vs FP4), and the specific harness used. Many users prefer Chinese models for these tasks because they have fewer refusals for security-related queries compared to American frontier models.
top insight
The effectiveness of these models is heavily dependent on the 'harness' (the agentic framework) rather than just the raw model intelligence; DeepSeek v4.1 Flash specifically performs significantly better when given a harness to iterate on solutions rather than in one-shot prompts. This allows users to run many more parallel agents for the same budget, effectively brute-forcing complex problems that might otherwise require a more expensive, 'smarter' model.
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