All articles
Catalog of AI/ML incidents, model failures, and confirmed adversarial-use cases. Each entry is dated, linked to its strongest source (advisory, paper, court filing or established reporting), tiered by reliability and tagged for analysts who need a reliable index, not a take farm.
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Deepfake Fraud Cases Explained: What 8 Incidents Show
Deepfake fraud cases explained: Arup's HK$200M video call, the WPP and Ferrari near-misses, FBI and FinCEN warnings, and what stops these scams.
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Chatbot Gone Wrong Examples: 12 Cases and What Failed
Twelve chatbot gone wrong examples, from Microsoft's Tay to Air Canada, Grok and McHire, with dates, root causes, outcomes and primary sources.
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Self-Driving Car Accident Causes: What Crash Data Shows
Sensor failures, software edge cases and fault attribution behind self-driving car accident causes, plus what NHTSA Standing General Order data shows.
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AI Incident Database Comparison: Which One to Use
AIID, OECD AIM, AIAAIC, AVID and MITRE ATLAS all answer to the name AI incident database. What each one records, and which to reach for when.
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AI Incident Reporting Requirements: Who to Notify
One AI incident can start four legal clocks at once. The EU AI Act, GDPR, SEC and sector regulators each want a different notice on a different deadline.
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Real-World AI Failure Examples: Arrest, Chatbot, Search
Three real-world AI failure examples: a wrongful arrest, chatbot liability and unsafe AI search answers, with sourced causes, outcomes and lessons.
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Best AI Monitoring Tools 2026: LLM Observability Compared
A practitioner's guide to the best AI monitoring tools for 2026, covering LLM observability platforms, ML drift detection, pricing, and how to choose.
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Prompt Injection Attack Explained: OWASP LLM01 and Examples
A technical explainer of how prompt injection attacks work, the direct and indirect attack classes, real consequences, and what defenders can do.
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Deepfake Detection Tools Review: Platforms Compared
A deepfake detection tools review of Reality Defender, Intel FakeCatcher, Pindrop Pulse and Sensity AI, plus C2PA provenance, with a buyer decision matrix.
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Reconstructing an Incident Timeline From Primary Sources
A vendor advisory, a CVE record, a regulator filing and a researcher's blog post all date one event differently. How to reconcile them into a timeline.
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An Incident-Response Playbook for AI Systems
Generic IR runbooks assume the failing component is a server you can patch. AI incidents add a model whose behavior you can't fully explain.
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Anatomy of a Vendor Advisory: Reading What Isn't Said
Vendor advisories from AI providers follow a recognizable shape. Knowing what to look for, and what is left out, turns marketing into usable signal.
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AI Taxonomy: Incident, Vulnerability, Disclosure, Misuse
A working taxonomy that distinguishes AI incidents, vulnerabilities, disclosures, and misuse by impact, weakness, publication, and intent.
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NVD CVE Entries for ML Libraries: What Fields Mean
NVD entries for torch, transformers, vLLM, and LangChain are not written for ML engineers. How to read the fields and what to do when metadata is thin.
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Reading a Model Card for Security Signals
Model cards are written for researchers, not defenders. What to read first, in what order, to judge a model's security posture and disclosure practice.
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Source Verification Tiers: Vetting an AI Incident Claim
A five-tier source ladder for verifying AI security incident claims, with evidence thresholds and examples of claims accepted or rejected.
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Why We Don't Do Attribution Speculation on AI Incidents
Attribution is the slowest, hardest, most consequential call in incident reporting. Here's the policy that keeps us from getting it wrong.
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AI Incident Logging: How We Log and Tag AI Incidents
The methodology behind AI Incidents — how we verify sources, date-stamp claims, and decide what's news vs noise in the AI security incident beat.