Prompt injection attacks exploit AI models to leak data or manipulate outputs

Prompt injection attacks exploit AI models to leak data or manipulate outputs by feeding them deceptive text. Palo Alto Networks and Unit 42 describe these attacks as a critical security risk for enterprises deploying AI systems, threatening potential data breaches, financial losses, and erosion of user trust.

Large language models process developer-built prompts and user inputs as a single instruction, creating a vulnerability attackers exploit. Palo Alto Networks and ZDNET note that attackers inject harmful commands directly into user input fields or indirectly through external data sources—such as web content or documents—that the AI processes. Stored prompt injection, a subtype of indirect attacks, embeds malicious instructions into the model’s memory or training data, altering its behavior over time.

Security assessments by Palo Alto Networks and Unit 42 reveal that three attack methods—guardrail bypass, information leakage, and goal hijacking—can achieve high success rates in mainstream large language models. Some techniques reach effectiveness rates of over 50%, with certain cases reaching up to 88%. These methods enable attackers to force AI systems to disclose sensitive information, such as business strategies, customer records, or security credentials, or to generate misleading outputs that distort decision-making processes.

When AI systems integrate with external tools capable of executing commands, attackers may exploit this to force the system into running unauthorized code, potentially leading to remote code execution or malware deployment, according to Palo Alto Networks and ZDNET.

Mitigation strategies include validating and sanitizing all user inputs and external data before they reach the model, as recommended by Palo Alto Networks, ZDNET, and Unit 42. Enforcing strict response templates and validating AI-generated outputs against safe patterns can also prevent unauthorized alterations. Limiting an AI system’s access to only essential functions, adhering to the principle of least privilege, further reduces exposure. Regular audits of system prompts and session-based controls that reset interactions help maintain defenses against prompt injection.

Individuals whose data may be exposed through AI systems should verify whether their organization implements input validation and least-privilege access controls, as advised by Palo Alto Networks and Unit 42.