Introducing CodeMender: an AI agent for code security
DeepMind has announced CodeMender, an AI agent designed to identify and fix critical software security vulnerabilities. The announcement comes from DeepMind's official blog, positioning it as an application of advanced AI to automated code security remediation. Further technical details are not available in the provided body text, but the agent appears to target real-world vulnerability patching workflows.
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Codex Security: now in research preview
OpenAI has launched Codex Security in research preview, an AI-powered application security agent. It analyzes project context to detect, validate, and patch complex vulnerabilities with the goal of higher confidence and reduced false-positive noise compared to traditional tools. The product extends OpenAI's Codex brand into the security domain.
Anthropic Launches Claude Code Security: AI-Powered Vulnerability Detection for Defenders
Anthropic has released Claude Code Security in limited research preview for Enterprise and Team customers, a capability built into Claude Code that scans codebases for security vulnerabilities and suggests patches for human review. Unlike rule-based static analysis tools, it uses Claude's reasoning to understand code context, trace data flows, and detect complex vulnerabilities including novel ones. Built on Claude Opus 4.6, the system found over 500 previously undetected vulnerabilities in production open-source codebases during internal research. The release is framed as a defensive measure to put AI-enabled vulnerability discovery in the hands of defenders before attackers can exploit the same capabilities.
DeepMind publishes AI Control Roadmap for securing internal agentic systems
DeepMind released a blog post outlining an AI Control Roadmap aimed at securing internal systems that use AI agents. The approach combines traditional security safeguards with real-time monitoring. The announcement signals DeepMind's formal internal posture on agentic AI safety and control.
DeepMind Publishes Framework for Evaluating Cybersecurity Threats of Advanced AI
DeepMind has released a framework designed to help cybersecurity experts assess and prioritize defenses against potential threats posed by advanced AI systems. The framework aims to systematically identify which defensive measures are necessary given AI's expanding capabilities in offensive cyber operations. This represents DeepMind's structured approach to evaluating AI-enabled cyber risks before they materialize at scale.
Introducing EVMbench: AI Agent Benchmark for Smart Contract Vulnerabilities
OpenAI and Paradigm have jointly introduced EVMbench, a benchmark designed to evaluate AI agents on their ability to detect, patch, and exploit high-severity vulnerabilities in Ethereum Virtual Machine (EVM) smart contracts. The benchmark targets a specialized security domain requiring both code understanding and adversarial reasoning. This represents a new evaluation surface for frontier AI agents in the context of blockchain security.
Protecting People from Harmful Manipulation
Google DeepMind has published research examining AI's potential for harmful manipulation across domains including finance and health. The work identifies manipulation risks and proposes new safety measures to address them. This represents a proactive safety research effort from a Tier 1 lab focused on misuse and adversarial deployment scenarios.
Google DeepMind launches $10M funding call for multi-agent AI safety research
Google DeepMind and unnamed partners have announced a $10M funding call targeting safety research for multi-agent AI systems. The initiative signals institutional recognition that multi-agent architectures present distinct safety challenges requiring dedicated research investment. This is a notable funding commitment from a tier-1 lab directed specifically at an underexplored safety domain.
Introducing SafeCoder
Hugging Face announced SafeCoder, an enterprise-focused code assistant product designed to run on-premises or in private cloud environments. The offering targets organizations that require data privacy and security guarantees, positioning it as an alternative to cloud-based coding assistants like GitHub Copilot. SafeCoder is built on top of open-weight code models and is sold as a managed solution for enterprise deployment.