Adversaries may input malicious prompts directly into an LLM’s user interface.^[1] The prompts can be designed to bypass safeguards, manipulate context, override previous instructions, or coerce the LLM into unauthorized behavior.^[2]

Adversaries may use direct prompt injection to Generate Content prohibited by the LLM's guardrails or to induce the system to utilize its capabilities in unintended or prohibited ways.^[3],[4] Adversaries may achieve this by instructing the LLM to ignore previous instructions or safety rules, or by impersonating a role such as a researcher or privileged user.^[5]

References

1. NIST. (n.d.). direct prompt injection. Retrieved September 18, 2026.
2. OWASP. (n.d.). LLM01:2025 Prompt Injection. Retrieved September 18, 2026.
3. Google Threat Intelligence Group. (2025, January). Adversarial Misuse of Generative AI. Retrieved September 18, 2026.
4. Palo Alto Networks. (n.d.). What Is a Prompt Injection Attack? [Examples & Prevention]. Retrieved September 18, 2026.
5. The CyberSec Guru. (2026, June 7). The Meta AI exploit: how a prompt injection flaw bypassed 2FA to steal million-dollar Instagram accounts. Retrieved September 18, 2026.

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