Topic
HackerSec
HackerSec's Yaga AI Agent Delivers Autonomous Penetration Testing with 98% Effectiveness
AI Security
Secure AI-Assisted Development: Five Critical Practices for Vibe Coding
AI SecurityHackerSec's Yaga Pentest Agent Reaches 98.8% Effectiveness in White Box Testing
AI SecurityAI-Powered Pentests Deliver Full Attack Chains Unlike Basic Vulnerability Scans
A new generation of AI-driven offensive testing tools is emerging that goes far beyond traditional vulnerability scanners. These AI agents perform reconnaissance, enumeration, business logic analysis, exploitation, and validation in a continuous adaptive loop. The result is not a long list of unconfirmed findings but validated vulnerabilities accompanied by technical descriptions, business impact, risk ratings, and working proof-of-concept evidence. True AI pentesting requires specialized agent architectures, memory, planning modules, and proprietary offensive tooling rather than generic prompts connected to existing scanners. In Brazil, HackerSec has built such a system with its Yaga agent, while XBOW and Aikido Security are recognized internationally. The technology is positioned to complement and eventually transform manual penetration testing practices.
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.