This wasn’t even a prompt-injection or context-poisoning attack. The vulnerable infrastructure itself exposed everything to hack into the valuable parts of the company:
Public JS asset → discover backend URL → Unauthenticated GET request triggers debug error page → Environment variables expose admin credentials → access Admin panel → see live OAuth tokens → Query Microsoft Graph → Access Millions of user profilesHasty AI deployments amplify a familiar pattern: Speed pressure from management keeps the focus on the AI model’s capabilities, leaving surrounding infrastructure as an afterthought — and security thinking concentrated where attention is, rather than where exposure is.
Fascinating research. The attack vector is straightforward: poison the RAG context, and the agent faithfully executes malicious instructions. This reinforces why external verification (high-SNR metrics) matters - without it, agents can’t detect when their ‘context’ has been compromised. Self-monitoring isn’t enough; you need ground truth outside the agent’s generation loop.
Seems like you’re talking about a different article: there was no context-poisoning, or in fact even anything LLM specific in this attack.
