Guidance:"Validate and sanitize all user inputs before sending to LLM. Use sentinel-core's 67 engines for real-time detection. Never trust LLM output for security-critical decisions without validation.",
Severity:"critical",
Standards:[]Reference{{Source:"OWASP LLM Top 10",Section:"LLM01",URL:"https://genai.owasp.org/llmrisk/llm01-prompt-injection/"}},
Guidance:"Never render LLM output as raw HTML/JS. Sanitize all outputs before display. Use Content Security Policy headers. Validate output format before processing.",
Severity:"high",
Standards:[]Reference{{Source:"OWASP LLM Top 10",Section:"LLM02"}},
},
{
Topic:"training_data",Title:"LLM03: Training Data Poisoning",
Guidance:"Verify training data provenance. Use data integrity checks. Monitor for anomalous model outputs indicating poisoned training data.",
Severity:"high",
Standards:[]Reference{{Source:"OWASP LLM Top 10",Section:"LLM03"}},
},
{
Topic:"denial_of_service",Title:"LLM04: Model Denial of Service",
Guidance:"Implement rate limiting (Shield). Set token limits per request. Monitor resource consumption. Use circuit breakers for runaway inference.",
Severity:"medium",
Standards:[]Reference{{Source:"OWASP LLM Top 10",Section:"LLM04"}},
Guidance:"Pin model versions. Verify model checksums. Use isolated environments for model loading. Monitor for backdoors in fine-tuned models.",
Severity:"high",
Standards:[]Reference{{Source:"OWASP LLM Top 10",Section:"LLM05"}},
},
{
Topic:"sensitive_data",Title:"LLM06: Sensitive Information Disclosure",
Guidance:"Use PII detection (sentinel-core privacy engines). Implement data masking. Never include secrets in prompts. Use Document Review Bridge for external LLM calls.",
Severity:"critical",
Standards:[]Reference{{Source:"OWASP LLM Top 10",Section:"LLM06"}},
Guidance:"Implement capability bounding (SDD-003 NHI). Use fail-safe closed permissions. Require human approval for critical actions. Log all agent decisions.",
Severity:"critical",
Standards:[]Reference{{Source:"OWASP LLM Top 10",Section:"LLM08"}},
Guidance:"Never use LLM output as sole input for security decisions. Implement cross-validation with deterministic engines. Maintain human-in-the-loop for critical paths.",
Standards:[]Reference{{Source:"OWASP LLM Top 10",Section:"LLM09"}},
},
{
Topic:"model_theft",Title:"LLM10: Model Theft",
Guidance:"Implement access controls on model endpoints. Monitor for extraction attacks (many queries with crafted inputs). Rate limit API access. Use model watermarking.",
Severity:"high",
Standards:[]Reference{{Source:"OWASP LLM Top 10",Section:"LLM10"}},