Building effective pen-testing agents (cecuro.ai)

Cecuro.ai has announced significant advancements in building effective pen-testing agents, achieving an impressive score of 91.45% on OpenAI's EVMBench, far surpassing competitors at 78.6%. Contrary to the belief that off-the-shelf AI models refuse to perform offensive tasks due to built-in guardrails, Cecuro found that the model itself does not inherently refuse such tasks; rather, a separate moderation classifier is responsible for these denials. Their research through over 10,000 agent transcripts revealed that the key to effective bug discovery lies more in context engineering and the orchestration of numerous agents than in fine-tuning the models themselves. The team's findings emphasize that the best performance is obtained not by relying on a single high-end model but by deploying multiple agents that examine various aspects of the codebase simultaneously. Notably, a median bug is often detected by multiple independent strategies, illustrating that breadth of approach is crucial for recall. This innovative perspective suggests that organizations can achieve substantial results using lower-cost models when effectively managed, underscoring the importance of strategy over sheer model power in security auditing. With LLM capabilities continually improving, running smart contracts through Cecuro.ai for fast and comprehensive audits is highly recommended for software developers.