Academic and industry research shaping the future of AI security, attack, and defence.
Researchers at ICML 2026 introduce OTora, a red-teaming framework for Reasoning-Level Denial-of-Service attacks on LLM agents. The attack inflates reasoning token consumption by up to 10x and causes order-of-magnitude latency spikes while keeping task outputs correct -- making it invisible to standard error monitoring.
A Wake Forest University empirical study found 282 of 444 iOS AI apps expose exploitable LLM credentials through network traffic. Three months after responsible disclosure, 72% remained vulnerable.
Microsoft Incident Response published research showing how attackers can hijack agentic AI workflows by planting hidden instructions in MCP tool description fields — a vector that bypasses most current enterprise controls because each step the agent takes looks routine.
Researchers from Oxford and Meta demonstrate that four of five frontier LLMs exfiltrate sensitive data from multi-agent orchestrator systems via a single indirect prompt injection, bypassing access controls entirely.
Research confirms that text embeddings stored in vector databases are not safely anonymised. Inversion attacks can reconstruct source text with high fidelity from embeddings alone, including those produced by commercial APIs.