14 posts tagged with "ai-security"

Your AI assistant just processed a contract from a prospective vendor. It summarized the terms, flagged the risky clauses, and drafted a response. What you don't know is that the PDF contained white text on a white background — invisible to your eyes, perfectly visible to the model — instructing it to recommend acceptance regardless of terms. The summary looks reasonable. The approval recommendation looks reasonable. The model followed instructions you never wrote.

This is the document-as-attack-surface problem, and most enterprise AI pipelines are completely unprepared for it.

The vulnerability is architectural, not incidental. When document content flows directly into an LLM's context window, the model has no reliable way to distinguish legitimate instructions from attacker-controlled content embedded in a file. Every document your pipeline ingests is a potential instruction source — and in most systems, untrusted documents and trusted system prompts are processed with equal authority.

Most multi-tenant LLM products have a security gap that their engineers haven't tested for. Not a theoretical gap — a practical one, with documented attack vectors and real confirmed incidents. The gap is this: each layer of the modern AI stack introduces its own isolation primitive, and each one can fail silently in ways that let one customer's data reach another customer's context.

This isn't about prompt injection or jailbreaking. It's about the infrastructure itself — prompt caches, vector indexes, memory stores, and fine-tuning pipelines — and the organizational fiction of "isolation" that most teams ship without validating.