{"data":{"id":"1e6205bd-2a95-4e27-9ec3-ceb57f40cd99","title":"Detecting and analyzing prompt abuse in AI tools","summary":"Prompt abuse occurs when attackers craft inputs to make AI systems perform unintended actions, such as revealing sensitive information or bypassing safety rules. Three main types exist: direct prompt override (forcing an AI to ignore its instructions), extractive abuse (extracting private data the user shouldn't access), and indirect prompt injection (hidden malicious instructions in documents or web pages that the AI interprets as legitimate input). The article emphasizes that detecting prompt abuse is difficult because it uses natural language manipulation that leaves no obvious trace, and without proper logging, attempts to access sensitive information can go unnoticed.","solution":"The source mentions that organizations can use an 'AI assistant prompt abuse detection playbook' and 'Microsoft security tools' to detect, investigate, and respond to prompt abuse by turning logged interactions into actionable insights. However, the source text does not provide specific details about what these tools are, how to implement them, or concrete technical steps for detection and mitigation. The full implementation details are referenced but not included in the provided content.","labels":["security","safety"],"sourceUrl":"https://www.microsoft.com/en-us/security/blog/2026/03/12/detecting-analyzing-prompt-abuse-in-ai-tools/","publishedAt":"2026-03-12T14:00:00.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["prompt_injection","jailbreak"],"issueType":"news","affectedPackages":null,"affectedVendors":["Microsoft"],"affectedVendorsRaw":["Microsoft","Google Gemini"],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-03-12T14:00:00.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["confidentiality","integrity","safety"],"aiComponentTargeted":"api","llmSpecific":true,"classifierConfidence":0.92,"researchCategory":null,"atlasIds":null}}