YagaBench

HackerSec's Yaga Pentest Agent Reaches 98.8% Effectiveness in White Box Testing

The offensive cybersecurity firm HackerSec announced that its Yaga pentest agent achieved a record 98.8% effectiveness in white box scenarios on the latest YagaBench evaluation. The agent also recorded 96.2% success in black box and 97% in gray box testing, marking the highest results since measurements began. These figures indicate that Yaga identified more than 98% of existing vulnerabilities across tested environments. The benchmark specifically highlights the performance gap between standalone AI models and the same models integrated into HackerSec's specialized pentest harness. Without the harness, models such as Opus 5 reached only 61% in white box testing, while GPT 5.6 SOL scored 60.9% in white box and 39.5% in black box. Yaga orchestrates four models during a single run, preserving context across phases and chaining findings to confirm exploitability while keeping false positives below 1%. CEO Andrew Martinez stated the company aims to reach 99% effectiveness across all pentest modalities by year end.

HackerSec Launches AI-Native Pentest Platform with Yaga Agent for Automated Offensive Testing

HackerSec has released a major update to its pentest platform introducing an AI-Native model where the Yaga agent handles full execution of offensive tests including reconnaissance, enumeration, exploitation, impact confirmation, and evidence production. Human experts only monitor operations and validate vulnerabilities while the AI maintains context, adapts strategies, and explores multiple attack paths until confirming exploitability. The platform addresses the growing use of AI by cybercriminals for scaled attacks and the expanding attack surface from rapid AI-driven development in enterprises. At its core, Yaga uses a proprietary harness coordinating multiple tools, playbooks, analysis stages, and AI models instead of relying on a single model. Benchmark results from YagaBench show the production configuration solving 91.2% of black box, 94.6% of gray box, and 93.5% of white box scenarios. CEO Andrew Martinez stated the goal of reaching over 98% effectiveness in all AI-Native pentests by year end. The approach enables more frequent testing with broader coverage and reduced time to identify vulnerabilities.