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AI security records indexed from public vulnerability, risk, and attack datasets.
Showing 1581-1600 of 3706 records
Factuality Errors is an AI risk in 3. Misinformation focused on 3.1 > False or misleading information. It is most relevant during 2 - Post-deployment.
ML Model Engineering groups 15 AI defenses for the AI lifecycle.
Hardware is AI attack method AML.T0010.000 with evidence level: feasible. It includes 1 related AI risk.
Defense Evasion is an ATLAS attacker goal with 15 related AI attack methods.
Restrict AI Agent Tool Invocation on Untrusted Data is an AI mitigation for Deployment, grouped as Technical - ML. It is connected to 3 attack methods.
Human In-the-Loop for AI Agent Actions is an AI mitigation for Deployment, grouped as Technical - ML. It is connected to 3 attack methods.
Vulnerability Scanning is an AI mitigation for ML Model Engineering, Data Preparation, grouped as Technical - Cyber. It is connected to 3 attack methods.
Restrict Library Loading is an AI mitigation for Deployment, grouped as Technical - Cyber. It is connected to 3 attack methods.
Use Multi-Modal Sensors is an AI mitigation for Business and Data Understanding, Data Preparation, ML Model Engineering, grouped as Technical - Cyber. It is...
Environment - Pre-Deployment is an AI risk in 7. AI System Safety, Failures, & Limitations focused on 7.0 > AI system safety, failures, & limitations. It is...
Environmental harms from operating LMs is an AI risk in 6. Socioeconomic and Environmental focused on 6.6 > Environmental harm. It is most relevant during 3...
Risks from data (Risks of data leakage) is an AI risk in 2. Privacy & Security focused on 2.1 > Compromise of privacy by leaking or correctly inferring sensi...
Reverse Exposure is an AI risk in 2. Privacy & Security focused on 2.2 > AI system security vulnerabilities and attacks. It is most relevant during 2 - Post-...
Verifiability is an AI risk in 7. AI System Safety, Failures, & Limitations focused on 7.4 > Lack of transparency or interpretability. It is most relevant du...
Transparency and explainability is an AI risk in 7. AI System Safety, Failures, & Limitations focused on 7.4 > Lack of transparency or interpretability. It i...
Benchmarking (Post-deployment contamination) is an AI risk in 6. Socioeconomic and Environmental focused on 6.5 > Governance failure. It is most relevant dur...
Extrinsic is an AI risk focused on X.1 > Excluded. It is most relevant during 2 - Post-deployment.
Defamation is an AI risk in 3. Misinformation focused on 3.1 > False or misleading information. It is most relevant during 2 - Post-deployment.
Entrenchment and exacerbation of existing inequalities is an AI risk in 6. Socioeconomic and Environmental focused on 6.1 > Power centralization and unfair d...
Privacy concerns is an AI risk in 5. Human-Computer Interaction focused on 5.1 > Overreliance and unsafe use. It is most relevant during 2 - Post-deployment.