AI Sec Watch

Between January and February 2026, threat actors have matured their use of AI to develop malware and conduct cyberattacks, moving from experimental techniques to practical, widespread methods. A single experienced developer with an AI-powered IDE (integrated development environment, a coding tool with AI assistance) can now accomplish what previously required entire teams, while the same AI tools that help businesses also create new security vulnerabilities that defenders must prepare to protect against.

Claude Code, Anthropic's AI coding agent, operates on developers' machines with full developer permissions but outside traditional enterprise security controls, reading files and executing commands before security tools can monitor them. Ceros is an AI Trust Layer (a security tool that sits on a developer's machine) built by Beyond Identity that provides real-time visibility, runtime policy enforcement, and an audit trail of Claude Code's actions by capturing device context, process history, and tying sessions to verified user identities through cryptographic keys.

RSA Conference 2026 is fundamentally organized around AI security, with 40% of sessions focused on how AI affects cybersecurity across all tracks. CISOs face a dual challenge: adopting AI quickly to stay competitive while simultaneously securing enterprise systems against new threats that AI itself creates. The conference prioritizes five learning areas: securing the AI stack (including RAG workflows, LLM data pipelines, and prompt injection attacks), AI governance and regulatory compliance, managing non-human identities (AI agents and service accounts that now outnumber human users), addressing shadow AI risks (unsanctioned tools and AI-generated code), and implementing autonomous security operations.

OpenAI has built a monitoring system for coding agents (AI systems that can autonomously write and execute code) used internally to detect misalignment, which occurs when an AI's behavior doesn't match its intended purpose. The system uses GPT-5.4 Thinking to review agent interactions within 30 minutes, flag suspicious actions, and alert teams so they can quickly respond to potential security issues.

The Trump administration has banned AI company Anthropic from Pentagon systems as a "supply chain risk," requiring government contractors to remove the company's technology within 180 days. However, most organizations lack complete visibility into where and how AI systems are used across their networks, making it extremely difficult to identify and remove Anthropic technology when it may be embedded in applications, APIs (application programming interfaces, which allow software to communicate), developer tools, or third-party services.

AI agents (autonomous programs that perform tasks without constant human input) face security risks when deployed in business environments, as a compromised agent could expose customer data or execute unauthorized actions. CrowdStrike Falcon AIDR (AI Detection and Response, a security monitoring system) now supports NVIDIA NeMo Guardrails (an open-source library that adds safety constraints to AI systems) as of version 0.20.0, enabling developers to add security controls like blocking prompt injection attacks (tricking an AI by hiding instructions in its input), redacting sensitive data, and moderating restricted topics.

OpenAI is acquiring Astral, a company that builds popular open source Python development tools like uv (for managing code dependencies), Ruff (for checking code quality), and ty (for type safety). After the acquisition closes, OpenAI plans to integrate these tools with Codex (its AI system for code generation) so that AI can work alongside the tools developers already use throughout their entire workflow, from planning changes to maintaining software over time.

Researchers successfully ran a very large AI model (Qwen 397B, a Mixture-of-Experts model where each response only uses a subset of the total weights) on a MacBook Pro by using Apple's "LLM in a Flash" technique, which stores model data on the fast SSD storage and pulls it into RAM as needed rather than keeping everything in memory at once. They used Claude to run 90 experiments and generate optimized code that achieved 5.5+ tokens per second (response speed) by quantizing (reducing precision of) the expert weights to 2-bit while keeping other parts at full precision. The final setup used only 5.5GB of constant memory while streaming the remaining 120GB of compressed model weights from disk on demand.

MLflow, a machine learning platform, has a vulnerability (CVE-2025-15031) in how it extracts model files from compressed archives. The issue is that the software uses `tarfile.extractall` (a Python function that unpacks compressed tar files) without checking whether file paths are safe, allowing attackers to use specially crafted archives with `..` (parent directory references) or absolute paths to write files outside the intended folder. This could let attackers overwrite files or execute malicious code, especially in shared environments or when processing untrusted model files.