Yaga

HackerSec's Yaga AI Agent Delivers Autonomous Penetration Testing with 98% Effectiveness

HackerSec has developed an autonomous penetration testing platform powered by its Yaga AI agent, marking a shift from scripted automation to adaptive, decision-making offensive security operations. Unlike traditional tools that follow predefined checks, Yaga observes the target environment, interprets responses, forms hypotheses, and replans attack paths in real time while maintaining memory and state across findings. The system relies on a proprietary harness that coordinates advanced AI models, security tools, contextual data, and ongoing decision logic rather than depending on a large language model alone. Yaga already achieves more than 98 percent effectiveness and supports continuous testing that matches the rapid change cycles of modern applications. CEO Andrew Martinez noted that periodic manual pentests can no longer keep pace with AI-accelerated attacks, requiring an AI-native model capable of operating at the same speed as the systems it protects. The platform allows human specialists to monitor and validate results while the agent conducts the investigation independently.

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.

AI-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.