LLM Indirect Prompt Injection: Covert Data Exfiltration v...
Definition
LLM indirect prompt injection occurs when an attacker embeds malicious instructions within data sources (e.g., web pages, documents, databases) that an LLM is subsequently directed to process. When a legitimate user prompts the LLM to interact with this compromised data, the LLM retrieves and interprets the embedded instructions, executing them as if they were part of the user's direct prompt, thereby bypassing direct input sanitization.
Why It Matters
This vulnerability enables covert data exfiltration, unauthorized API invocations, and privilege escalation by manipulating the LLM's operational context. Since the malicious payload resides in external data rather than the user's direct input, traditional prompt sanitization is ineffective, leading to silent compromise of sensitive systems, data leakage, or arbitrary code execution via tool-use.
How Exogram Addresses This
Exogram's 0.07ms deterministic policy engine operates at the AI execution boundary, intercepting all LLM-generated outputs and tool calls *before* they are materialized. By enforcing granular, context-aware rules on data access patterns, API endpoints, and command structures, Exogram can detect and block unauthorized actions originating from an indirectly injected prompt, preventing data exfiltration or system compromise even if the LLM was successfully manipulated.
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Related Terms
Key Takeaways
- → This concept is part of the broader AI governance landscape
- → Production AI requires multiple layers of protection
- → Deterministic enforcement provides zero-error-rate guarantees