Positive Technologies Enhances PT Dephaze with Local LLM for Automated Credential Discovery and Attack Expansion
Positive Technologies has updated its automated penetration testing platform PT Dephaze. The new version incorporates a built-in local language model designed to locate login credentials and autonomously expand test attacks.
The local LLM scans configuration and text files stored in network folders, identifies potential account details, and tests whether the discovered combinations grant access to corporate services. Invalid pairs are filtered out while valid credentials are reused to further the simulated attack. Because the model ships inside the product distribution, no processed information is sent to external services or the internet.
After the automated test completes, security teams can remove files containing forgotten passwords and other sensitive data before real attackers locate them. The update also broadens attack coverage against Unix and Linux environments, allowing the tool to exploit suitable vulnerabilities for privilege escalation, extract credentials from compromised hosts, and attempt reuse across additional services.
Support for FreeIPA directory services has been added, covering environments that include import-substituted infrastructures. PT Dephaze can now detect such domains, collect account information, validate passwords, and anonymously dump user lists when the service configuration allows it.
Positive Technologies describes the LLM integration as the initial phase of AI capability development for PT Dephaze, with plans to introduce additional attack techniques targeting Russian operating systems in future releases.
Related articles
Russian State-Linked Group GTG-20006 Uses Anthropic AI Agents to Automate Malware Rebuilding
Anthropic has identified a Russian state-linked operation tracked as GTG-20006 that deployed autonomous AI agents to continuously rebuild its malware arsenal whenever detections occurred. The group, connected to Midnight Blizzard, APT29 and Cozy Bear, created a closed-loop automation system in which AI agents monitored tool performance against known defenses and triggered immediate code modifications to evade security products. Beyond malware, the agents handled domain registration, hosting infrastructure setup, phishing email delivery, command-and-control channel monitoring and implant persistence tracking across compromised environments. The campaign, active in July and August 2026 and overlapping with CaptiveCrunch, targeted more than twenty organizations including ministries, defense bodies, embassies and think tanks across Ukraine, Europe, the Middle East and Asia. In one incident the attackers exfiltrated over 300,000 national identity records and commercial registration data for more than 500,000 companies. Anthropic disrupted the activity and published a detailed report highlighting how the automation shifted the cost burden back onto defenders.
Anthropic Exposes Widespread Weaponization of Claude by Nation-State Hackers and Cybercriminals for Automated Attacks
Anthropic has released a threat intelligence report detailing how multiple state-sponsored and criminal groups systematically abused its Claude model between December 2025 and August 2026. The company introduced the term Generative Threat Groups to describe actors that built multi-agent frameworks to automate reconnaissance, exploitation, and data exfiltration. One group identified as GTG-20006, widely linked to Midnight Blizzard, APT29 and Cozy Bear, created an AI-driven workflow that automatically rewrites and redeploys malware once security tools detect it. The report highlights that this capability collapses the traditional gap between well-resourced nation-state operations and individual attackers. Defensive recommendations focus on shifting detection to behavioral chains, shortening IOC validity periods, strengthening data-loss prevention, and establishing internal governance for AI tool usage.
Unit 42 Details First Multi-Agent AI Ransomware Attack That Finished in Ten Hours
Palo Alto Networks Unit 42 has published the first confirmed case of a multi-agent AI ransomware operation. Attackers only defined the target; more than ten specialized AI agents then performed reconnaissance, credential harvesting, lateral movement, data exfiltration, and encryption within ten hours. The agents used over fifty ATT&CK techniques and successfully hid command traffic inside the victim’s own AI service endpoints. After encryption the same agents automatically generated an eighty-page security audit report listing every compromised system and technique. The sole defensive control that stopped part of the attack was a mandatory multi-person code review rule on Terraform changes. Unit 42 links the operation to frontier large-language-model frameworks and notes that earlier single-agent incidents such as JADEPUFFER have now evolved into coordinated agent fleets.
Deepfakes Turn Job Interviews into Cyberattack Vectors Targeting IT Candidates and Recruiters
Deepfake technology and malicious test assignments are increasingly used during IT hiring processes to conduct industrial espionage or deploy malware. Attackers impersonate recruiters or candidates, sending infected GitHub repositories or npm packages that install backdoors stealing credentials and enabling remote access. Groups such as Lazarus and the dedicated Contagious Interview collective have run campaigns against chemical and IT firms, while individual cases like the Smello Python developer incident show how prepare scripts in package.json can trigger hidden payloads. Gartner predicts that by 2028 one in four job applicants could be fake, creating risks beyond bad hires including data theft and financial loss. Defenses include isolated virtual machines for test tasks, profile verification by companies like Socure, and interview techniques such as the GOTCHA movement challenges or corneal reflection probes developed by universities. Major firms including Cisco, McKinsey, and Google are returning to in-person interviews as a reliable countermeasure. The rapid evolution of deepfake quality tracked by Unit 42 means layered verification combining technical, procedural, and human checks is now essential.