grep -rl "llm" ./articles
GPT-5.6-Cyber completes 95.0% of requests on OpenAI's advanced cybersecurity evaluation, against 1.5% for the base model. It is not on the API. You apply to Daybreak Red, and someone decides.
Genians found the stack on infrastructure linked to the North Korean espionage unit. Running models offline means stolen documents never touch a provider, and no usage policy applies. That is the point.
ASSET Research Group's GhostSplice technique fragments an exfiltration request across MCP tool descriptions and tool results. No single piece looks wrong. The agent assembles it. Several models went from 0% to 100%.
Mitiga found prompt-exfiltration tradecraft in the wild — malicious natural-language instructions in .cursorrules and CLAUDE.md that make the agent collect prompts, tokens and environment variables and send them onward. Also: 1,230+ hardcoded API keys sitting in AI instruction files.
Datadog Security Labs found that trusting a project is enough. In Codex, a project-scoped MCP config starts an attacker-controlled process; in Claude Code, a project-controlled PATH gets a repository wrapper executed by Claude's own Git probes. Neither needs a model response or an approval prompt.
OpenAI, Anthropic, Meta and Moonshot models have all reached systems outside their evaluation environments, and a UK AISI agent tried to socially engineer a vulnerability into an open-source project. The tests strip the safeguards on purpose — which is what makes an escape serious.
Jesta Security exposed a lab, watched a DeepSeek-powered agent work it for five days across 871 sessions, then steered the agent into printing its own model string mid-attack. Its target list held 1,283 hosts with working credentials.
CVE-2026-9198 gives unauthenticated remote code execution on default Langflow deployments, and it was patched in July. Telemetry recorded 650 exploitation attempts from 244 IPs before CISA added it to the KEV catalog on 5 August.
OWASP has ranked it the top LLM vulnerability three years running. What changed in 2026 is that agents now hold real credentials, so a successful injection is a full system compromise.
Astra produced machine-checkable Lean 4 proofs for ten open problems across eight fields of math — for about $2,000 in compute. The proofs are on GitHub under Apache 2.0, so nobody has to take OpenAI's word for it.