The enterprise rush to deploy LLMs has created a massive, under-secured attack surface. Traditional security audits (checking against known vulnerability databases) are critically insufficient for stochastic (probabilistic) models. The real threat is Prompt Injection—maliciously crafted inputs that trick the model into ignoring its safety guardrails, leaking proprietary data, or executing unauthorized code.
We detail three sophisticated adversarial vectors that standard audits miss:
- Recursive Prompt Injection: Forcing the model to interpret its own output as a command, bypassing initial input filters.
- Indirect Prompt Injection via Data Retrieval: Hiding instructions within untrusted external data sources (like web scrapes or emails) that the model processes.
- Adversarial Token Manipulation: Using subtle, often non-readable token sequences that flip model behavior without triggering keyword-based defensive layers.
The Datasoli Approach: We argue that true AI security requires continuous, automated Adversarial Red Teaming that stress-tests the model in a sandbox environment against live, simulated attacks before every deployment.
