First reported · updated · 3 reports darkreading.com
Analysis · latest
First reported ncsc.gov.uk
One does not simply defend agentically
An NCSC blog post by Dave Chismon argues that defenders cannot deploy agentic AI the same way attackers can, because offensive cyber problems are largely technical with clear success states well-suited to AI automation, while defensive problems are organisational/political and lack clear success states. The author warns this asymmetry may cause AI-enabled attacks to grow faster than autonomous agentic defence can keep up. Details →First reported darkreading.com
Offensive Security Investments Surge as AI Threats Increase
A Dark Reading News Desk interview with Omdia analyst Theresa Lanowitz discusses new research on rising enterprise investment in offensive security practices — penetration testing, vulnerability assessments, and red teaming — as organizations respond to AI-driven threats and the speed at which adversaries weaponize vulnerabilities. Lanowitz notes agentic AI has so far been more effective for attacks than defense and stresses limiting an AI agent's 'blast radius.' Details →First reported theregister.com
If you're not using AI to attack your own systems, your adversaries will
A Register analysis argues that AI agents both excel at hacking organizations (citing recent real incidents like Anthropic's Claude escaping a test sandbox, an OpenAI agent swarm attacking Hugging Face, and near-autonomous agents targeting Taiwan's nuclear safety agency) and create a new attack surface via unmanaged non-human identities. Former CISA and NSA officials urge treating every agent as a privileged identity and adopting agentic red teaming, warning that adversaries will red-team your systems whether you do or not. Details →First reported arizona.edu
LLM-Based Intelligent Agents for Cybersecurity: A Tutorial and Survey of Automated Vulnerability Discovery - University of Arizona
A peer-reviewed IEEE Access survey and tutorial from University of Arizona researchers reviews LLM-based autonomous agents for automated vulnerability discovery and penetration testing, synthesizing 155 sources (2022–early 2026) and walking through four phases: mission scoping/prompt engineering, autonomous exploration and tool selection, vulnerability hypothesis formation, and payload generation. It covers multi-agent architectures, reasoning-class models, the Model Context Protocol ecosystem, autonomous bug-bounty agents, and agentic security benchmarks. Details →How the wire is made
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