Autonomous defense

Google's Product Security team describes PageBreak, an internal agent mostly using Gemini models that hunts vulnerabilities in Google's first-party web applications and only reports findings confirmed by non-AI validators against running applications. Google reports over 500 XSS vulnerabilities found with near-zero false positives, while apps on its high-assurance web frameworks yielded only 2 XSS bugs as of 4 September 2026.

The Trump administration's Cyber Strategy for America commits to securing the AI technology stack, adopting AI-powered cybersecurity for federal networks, and using AI-enabled tools to detect, divert and deceive threat actors. It states the US will rapidly adopt and promote agentic AI to securely scale network defense and disruption.

Anthropic reports that Pacific Northwest National Laboratory built a scaffold around Claude Sonnet 4 to automate adversary emulation against a high-fidelity cyber-physical model of a water treatment plant used for CISA. PNNL estimates attack reconstruction took three hours instead of multiple weeks; in one run the model switched to a different known privilege-escalation technique when a provided tool failed.

NIST published a preliminary draft Cybersecurity Framework Profile for Artificial Intelligence, aligned with CSF 2.0. It is organized around three focus areas: securing AI systems, using AI for cyber defense, and thwarting AI-enabled cyberattacks, with comments due January 30, 2026.

CISA and the Australian Signals Directorate, with NSA, FBI and national cyber agencies of Canada, Germany, the Netherlands, New Zealand and the UK, published four principles for integrating AI into operational technology. The guidance explicitly covers machine learning, LLM-based AI and AI agents because of the security and safety challenges they pose in industrial environments.

Microsoft reports a randomized controlled trial of its own Security Copilot Phishing Triage Agent. In the trial, 167 external security analysts each triaged a 25-email queue drawn from a curated corpus of emails reported by Microsoft employees. In the scenario where the agent classified every corpus email correctly, analysts with the agent found 6.5 times as many true positives per minute as the control group and scored 77% higher on F1; with the agent's accuracy set to 80% and a 20% malicious rate, the productivity gain fell to 3.1 times. Analysts with the agent spent 53% more time on malicious emails and did not simply confirm its malicious verdicts, but they were more likely to accept its benign verdicts, including planted false negatives.

A paper in Applied AI Letters argues that single-objective RL defenders built on hand-weighted rewards cannot adapt at inference time to competing goals such as stopping intrusions versus avoiding downtime. It presents a simple multi-objective network defence game in which defending against red agents must be balanced with preserving network services.

A systematic review in ACM Computing Surveys covers autonomous blue- and red-team agents and cyber operations environments, and proposes criteria for judging whether autonomous network defence is ready for real deployment. It identifies gaps in explainability, continual learning under evolving threats, and realistic training environments.

DARPA reports that seven finalist cyber reasoning systems analyzed over 54 million lines of code, found 54 unique synthetic vulnerabilities in 63 challenges and patched 43, and found 18 real non-synthetic vulnerabilities with 11 patches. Team Atlanta won $4 million, Trail of Bits $3 million and Theori $1.5 million; DARPA and ARPA-H added $1.4 million for real-world integration and four systems were open-sourced on the day.

Microsoft Research describes Project Ire, a prototype LLM agent that uses decompilers and binary analysis tools to reverse engineer software and classify it as malicious or benign, producing an auditable chain-of-evidence report. Microsoft reports 0.98 precision and 0.83 recall on a Windows driver dataset, but 0.26 recall on about 4,000 hard real-world files, and plans to deploy it in Defender as Binary Analyzer.

A paper in Applied AI Letters by QinetiQ researchers sets out a test and evaluation process for cyber defence agents covering performance, effectiveness, resilience and generalisability, and demonstrates its low-fidelity stage on CAGE Challenge 2 RL agents in CybORG. It introduces Measures of Effectiveness tailored to cyber defence alongside RL reward and tests agents under environment perturbations not seen in training.

Researchers led by UC Santa Cruz integrated LLM agents into the CybORG CAGE 4 multi-agent defence environment and proposed a communication protocol for mixed LLM and RL teams. In their runs an all-RL team scored far better reward than an all-LLM (GPT-4o-mini) team and acted about 104 times faster, though the authors highlight LLM explainability and note the environment was designed for RL agents.

Microsoft announced Microsoft-built Security Copilot agents, including a Phishing Triage Agent in Defender, alert triage agents in Purview, a Conditional Access Optimization Agent, a Vulnerability Remediation Agent in Intune and a Threat Intelligence Briefing Agent, plus five partner agents. Preview was planned from April 2025; the announcement contains no evaluation of agent accuracy.

The Frontier Model Forum, an industry body of frontier labs, published an issue brief on using frontier AI for cyber defense. It lists use cases including process automation for incident response, natural-language querying and analysis, vulnerability discovery and fixing, open-source intelligence and training, and recommends designing for human-AI collaboration rather than full automation.