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CREST Onboards First Cohort for AI-Enabled Pentesting Accreditation
CREST accredited 10 companies, including ImmuniWeb and Thoropass, in the first cohort of its new AI-Enabled Penetration Testing accreditation.
CREST granted its AI-Enabled Penetration Testing accreditation, an optional module added in July 2026 to its Penetration Testing Accreditation Standard, to a first cohort of 10 firms spanning Europe, India, the US, and Canada, including Closed Door Security, ImmuniWeb, JUMPSEC, Packetlabs, Pentesys, REDSECLABS, Risk Associates, SECNORA, Solusec, and Thoropass. The module lets providers that integrate AI into pentesting demonstrate responsible AI governance through independent assessment. A March 2026 CREST report found 76% of cybersecurity providers increased AI usage year over year and 69% already integrate it into daily service delivery; its June AI Charter has been signed by over 100 organizations.
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Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives
Paper proposes a threat taxonomy and guardrail analysis for LLM-powered autonomous penetration testing agents, covering lifecycle, architecture, and behavioral attacks.
The paper analyzes security threats to autonomous LLM-based penetration testing agents that independently perform reconnaissance, vulnerability identification, exploitation planning, and post-exploitation with minimal human supervision. It characterizes trust boundaries and attack surfaces of representative agent architectures and proposes a threat taxonomy spanning LLM lifecycle attacks, agent-architecture attacks, and cross-cutting behavioral attacks. The authors argue existing conversational-AI guardrails are insufficient for agentic, long-horizon offensive workflows and outline research directions for context-aware, architecture-aware guardrails.
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PentestGPT: Open-source automated penetration testing agentic framework
Open-source PentestGPT runs autonomous LLM-driven penetration tests via Claude Code and Codex, with legacy human-in-the-loop mode supporting many providers.
PentestGPT, originally published at USENIX Security 2024 by Gelei Deng and colleagues, is an open-source framework that lets a large language model autonomously run penetration testing stages (recon, exploit, walkthrough) with no human in the loop, driving Claude Code or Codex CLIs. A legacy interactive mode uses three cooperating LLM sessions maintaining a Pentesting Task Tree and supports OpenAI, Anthropic, Google Gemini, DeepSeek, xAI, Qwen, Moonshot, and local models via Ollama. The tool sends anonymous telemetry to Langfuse by default, excluding command outputs, credentials, and flags, and is available free on GitHub.
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