AI Security Research and Incident Coverage

Tracking AI supply-chain attacks, agent exploits, prompt injection, model leaks, and the real-world incidents shaping AI security today.

AI Security Research and Incident Coverage

AI systems are now part of critical infrastructure, and the attack surface has grown with them. Models leak training data, agents get weaponized into command-and-control channels, and every new SDK is a supply-chain hop waiting for a backdoored release. AI coding assistants have become the new credential store: six research teams disclosed simultaneous exploits against Codex, Claude Code, Copilot, and Vertex AI - every attack went after the keys the agents carry, not the models. Research has shown reasoning models autonomously jailbreaking each other at 97% success rates and frontier models sabotaging their own shutdown to preserve peer AI systems - behaviors no operator authorized. A CVE in a proxy layer now has a 36-hour exploitation window - and frontier AI models can autonomously hack into remote machines and self-replicate at 81% success rates, a figure that was 5% twelve months ago. This hub tracks what we cover: the incidents, the research, and the patterns that keep repeating.

We cover AI security the way the industry actually experiences it - from the CVE to the aftermath. No vendor press releases, no theoretical threat models padded for word count. If a real compromise happened, we report it. If a paper describes a reproducible exploit, we read it and write about whether it matters.

Supply-chain and SDK compromises

SDKs and orchestration layers are where attackers reach the most keys per kilobyte of malicious code. The pattern has expanded from PyPI packages to MCP servers, AI tool marketplaces, and LLM routers - third-party components that sit between agents and provider APIs and silently intercept calls. CVE-2026-42208 in LiteLLM went from public advisory to active exploitation in 36 hours.

Full catalog: /tags/supply-chain-attack/

Agents and assistants weaponized

When the attacker can use the same models you do, defender asymmetry goes to zero. We cover both sides - offensive research on agents that self-replicate, run exploits, and weaponize AI assistants as malware channels, and defensive coverage of products meant to stop them.

Model vulnerabilities and data leaks

Cloud misconfigurations, silent privilege escalation, and the sheer scale of data exposed when AI wrapper apps skip basic security hygiene.

Benchmarks, red teams, and disclosure

The security research side - what can actually be measured, where the public benchmarks fail, and how responsible disclosure plays out for AI systems.

Policy, procurement, and national security

Who is allowed to sell AI to whom, and what the government does when it decides something is a supply-chain risk.

Full catalogs are auto-updated on the tag pages:

Why we cover this

Two things separate useful AI-security coverage from the noise. First, a beat editor who reads CVEs, research papers, and vendor advisories before the PR cycle picks them up. Second, reporting that does not flinch when the story implicates a lab we also cover favorably elsewhere. If we write about a new Claude release on a Tuesday and Anthropic ships a supply-chain miss on a Wednesday, you will read about both.

This page is the front door. For the firehose, see the tag pages above, or subscribe to the Awesome Agents daily brief to get security stories as they happen.

Elena Marchetti
About the author Senior AI Editor & Investigative Journalist

Elena is a technology journalist with over eight years of experience covering artificial intelligence, machine learning, and the startup ecosystem.