HackerSec Launches AI-Native Pentest Platform with Yaga Agent for Automated Offensive Testing
HackerSec, an offensive cybersecurity company, has launched a major update to its pentest platform featuring a new Pentest AI-Native technology. In this model, an AI agent takes over complete execution of offensive tests while human specialists only monitor the operation and validate each vulnerability.
The platform performs reconnaissance, enumeration, validations, exploitations, impact confirmation, and evidence production autonomously. Cybercriminals are already deploying AI agents to automate reconnaissance, identify vulnerabilities, and scale offensive campaigns. At the same time, organizations across sectors accelerate development with AI, producing more updates, more production code, and a constantly expanding attack surface that requires pentesting to keep pace.
In the AI-Native model, the intelligence receives the scope, investigates the environment, interprets system responses, formulates hypotheses, and adapts its strategy based on results. The agent maintains full operational context and continues exploring different paths until confirming whether a vulnerability is truly exploitable, enabling complete, continuous testing with reduced human intervention.
At the center of the platform is Yaga, the pentest agent developed from scratch by HackerSec. The technology uses a proprietary harness that coordinates tools, playbooks, multiple analysis stages, and different AI models during the same execution. Instead of depending on the reasoning of a single model, Yaga combines distinct capabilities to interpret the target, select techniques, execute authorized attacks, and technically validate findings.
Results published on YagaBench demonstrate the impact of this multi-model architecture. The production configuration of Yaga, operating with four combined models, solved 91.2% of black box scenarios, 94.6% of gray box scenarios, and 93.5% of white box scenarios. This performance highlights that the platform's advantage lies in the proprietary offensive orchestration layer developed by HackerSec.
“Our goal is to achieve, by the end of the year, more than 98% effectiveness in all AI-Native pentests with Yaga,” commented Andrew Martinez, CEO of HackerSec. New cyber environments change at a speed that no longer matches point-in-time pentests, long cycles, and high dependence on manual execution. The AI-Native model allows companies to perform offensive testing more frequently, expand coverage, and reduce the time between vulnerability emergence and identification while keeping human experts responsible for monitoring and technical validation.
Related articles
Fine-Tuning Prompt Injection Detector: Five Rounds, Four Failures and Gates Against Regression
AGIMA technical director Andrey Nepryakhin details the challenges of adapting an open-source GLiNER-based prompt injection detector for Russian corporate traffic. The system processes nearly one million requests monthly from eighty employees using agentic tools. Five fine-tuning rounds using LoRA adapters on microsoft/mdeberta-v3-base were conducted, yet only the second round reached production. Failures stemmed from regression on previously fixed attacks, imbalanced negative samples, and reliance on rule-based signals instead of the model. The team introduced cumulative corpora, person-based splits, and ablation checks against the base model to prevent silent rollbacks. A gradient mass rule limits any single phenomenon to 10-20 percent of training data to avoid precision or recall collapse.
Secure AI-Assisted Development: Five Critical Practices for Vibe Coding
AI has become the default path for quickly turning ideas into working applications, yet most security flaws in these projects stem not from model errors but from developer assumptions. The AI simply implements what is requested, and security requirements are rarely included in prompts. Five key areas account for the majority of issues: explicitly defining what an application must not do, distinguishing authentication from authorization, reviewing AI-suggested dependencies, removing secrets from commit history, and validating business logic rules that automated scans cannot detect. Applications built this way are already attracting cybercriminals because the resulting vulnerabilities can be identified and exploited at scale. The HackerSec Pentest Platform is positioned as an accessible option for vibe coders seeking quality security testing before production deployment.
Selectel Launches Local AI Admin Agent aish in SELECTOS to Eliminate Cloud Data Risks
Selectel has introduced aish, a generative AI agent embedded directly into its SELECTOS server operating system. The solution allows system administrators to analyze incidents, review logs, and perform routine operations entirely on-premises without transmitting sensitive data to external cloud providers. Aish operates with a human-in-the-loop model, generating proposed commands and explanations that must be approved by an operator before execution. The primary goal is to support organizations bound by strict data-protection policies, including compliance with Russian Federal Law 152-FZ, by keeping all context within local infrastructure. SELECTOS is based on Debian and is distributed in ISO, QCOW2, and container formats for both cloud and dedicated servers. According to Kirill Dmitriev, Director of System Software at Selectel, the agent is intended to lower the entry barrier for Linux system administration while respecting restrictions on the use of foreign large language models.
Three-Phase Defense Model OGL-Mini Protects AI Agents from Prompt Injection and Modern LLM Threats
The article presents OGL-Mini, an open-source hybrid security model designed to defend AI agents, chatbots, and RAG systems against contemporary threats including prompt injection, system prompt leakage, and agentic attacks. It details real-world incidents from 2025-2026 involving Microsoft Copilot Studio, OpenAI Atlas, and Claude Code, showing how attackers bypass safety filters using structured formats and obfuscation. OGL-Mini employs a three-stage pipeline of heuristics, TF-IDF mini-classifier, and PII detection to intercept malicious inputs before they reach the LLM. The model was trained on over 110,000 examples covering OWASP LLM01 categories, agentic misuse, and modern obfuscation techniques. Available in TypeScript, Python, and Go, it runs efficiently on standard CPUs with low latency. The solution aims to address gaps in built-in LLM safeguards that remain vulnerable to techniques like Policy Puppetry.