Adversaries may deliver malicious prompts to an LLM indirectly by embedding the prompts in content that the adversary expects may be processed by an LLM.^[1] These prompts can be included in web pages,^[2] documents, emails, tickets,^[3] code, or other sources that an agent, copilot, or other AI system may retrieve and process. When an LLM processes this content, the embedded instructions may cause the system to perform unintended behaviors.^[4]

Adversaries may use indirect prompt injection to Impair Autonomous Defenses, and for unauthorized execution of any capabilities available to the LLM.

References

1. NIST. (n.d.). indirect prompt injection. Retrieved September 18, 2026.
2. Beliz Kaleli Shehroze Farooqi Oleksii Starov Nabeel Mohamed. (2026, March 3). Fooling AI Agents: Web-Based Indirect Prompt Injection Observed in the Wild. Retrieved September 18, 2026.
3. Stephen Thoemmes. (2026, February 19). How “Clinejection” Turned an AI Bot into a Supply Chain Attack. Retrieved September 18, 2026.
4. Google Threat Intelligence Group. (2026, September 8). GTIG AI Threat Tracker: From Prompting to Autonomy – The Evolution of Adversarial AI. Retrieved September 18, 2026.

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