PT Dephaze

Automated Pentesting and BAS: How AI Systems Like XBOW Outpace Human Researchers in Vulnerability Discovery

The article explores how automated penetration testing and Breach and Attack Simulation tools have evolved to provide continuous validation of security controls beyond annual manual pentests. It explains the distinction between BAS, which tests individual attack techniques against security tools using frameworks like MITRE ATT&CK, and autopentest solutions that build complete attack paths to critical assets. Russian vendor Positive Technologies released PT Dephaze 3.0 in October 2025, incorporating machine learning for controlled internal pentesting and earning the National Runet Award. Globally, AI-driven systems demonstrated superior performance, with XBOW topping HackerOne rankings by discovering over 1,000 vulnerabilities including 54 critical ones in just 90 days. Google’s Big Sleep project, combining DeepMind and Project Zero, identified and helped patch CVE-2025-6965 in SQLite before widespread exploitation. These developments underscore the need to integrate automated validation into vulnerability management processes under the emerging CTEM framework.

Positive Technologies Enhances PT Dephaze with Local LLM for Automated Credential Discovery and Attack Expansion

Positive Technologies has released a major update to its automated penetration testing system PT Dephaze, integrating a local large language model that actively searches for login credentials and develops attacks. The LLM analyzes configuration and text files across network shares to locate potential usernames and passwords, then validates them against discovered corporate services while discarding invalid combinations. Working credentials are immediately used to extend the simulated attack chain, including privilege escalation on Unix and Linux systems and credential reuse across multiple services. The update also adds support for FreeIPA directory services commonly found in import-substituted infrastructures, enabling anonymous user enumeration when configurations permit. All processing occurs locally within the product distribution, ensuring no data leaves the customer environment. Positive Technologies positions this LLM integration as the first step in expanding AI-driven attack techniques, with future plans focused on Russian operating systems.

Rent, Don't Build: Penetration Testing, Vulnerability Management, and Bug Bounty as a Service

The fifth installment in the Vulnerability Management for Beginners series explores three outsourcing models that allow organizations to avoid building their own infrastructure for vulnerability management. Pentest as a Service, VM as a Service, and bug bounty programs each offer distinct advantages in speed, coverage, and cost while shifting operational burdens to specialized providers. The article details how automated and manual penetration testing differ in depth and scope, why white-box scanning inside the network yields more accurate results than black-box perimeter tests, and how bug bounty platforms like HackerOne and Standoff Bug Bounty have scaled dramatically in Russia and globally. It also covers practical requirements such as proving asset ownership, establishing triage processes, setting realistic SLAs, and handling sensitive data risks when using external services. Benchmarks from providers, updated EPSS metrics, CISA KEV catalogs, and the latest FSTEC methodology are presented as tools for better prioritization. The piece concludes with legal and operational caveats for Russian companies adopting these models in 2025.