Prompt Injection Risk

A Prompt Injection Risk is a security risk that can compromise AI system behavior through malicious prompt manipulation.

• AKA: Prompt Attack Risk, Input Manipulation Risk, AI Security Vulnerability.
• Context:
  • It can typically exploit Input Processing through adversarial prompts.
  • It can typically bypass Safety Guardrails through crafted instructions.
  • It can typically manipulate System Behavior through context hijacking.
  • It can typically extract Sensitive Information through prompt engineering.
  • It can typically override System Instructions through priority exploitation.
  • ...
  • It can often target Multi-Modal Systems through cross-modal attacks.
  • It can often exploit Context Windows through overflow techniques.
  • It can often leverage Role Playing through persona manipulation.
  • It can often utilize Encoding Tricks through obfuscation methods.
  • ...
  • It can range from being a Low-Severity Prompt Injection Risk to being a Critical Prompt Injection Risk, depending on its risk impact level.
  • It can range from being a Direct Prompt Injection Risk to being an Indirect Prompt Injection Risk, depending on its risk attack vector.
  • It can range from being a Detectable Prompt Injection Risk to being a Stealthy Prompt Injection Risk, depending on its risk visibility.
  • ...
  • It can integrate with Security Assessment Tools for vulnerability detection.
  • It can connect to Monitoring Systems for attack identification.
  • It can utilize Defense Mechanisms for risk mitigation.
  • It can implement Audit Systems for incident tracking.
  • ...
• Example(s):
  • Direct Injection Risks, such as:
    • System Prompt Override Risk for instruction replacement.
    • Jailbreak Attempt Risk for restriction bypass.
    • Role Confusion Risk for identity manipulation.
  • Indirect Injection Risks, such as:
    • Data Poisoning Risk for training corruption.
    • Context Contamination Risk for memory pollution.
    • Chain Injection Risk for multi-step exploitation.
  • Application-Specific Risks, such as:
    • Customer Service Bot Risk for unauthorized actions.
    • Code Assistant Risk for malicious code generation.
    • Content Filter Risk for moderation bypass.
  • ...
• Counter-Example(s):
  • Legitimate Prompt Variations, which lack malicious intent.
  • System Errors, which lack adversarial design.
  • User Mistakes, which lack exploitation objective.
• See: AI Security, Adversarial Attack, System Vulnerability, Security Mitigation.

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