Documented AI Incidents
Hallucinations, ghost sources, RAG failures: understand and prevent common AI agent issues.
AI Hallucinations - RAG best practices - Ghost sources - KB Drift - Chunking strategies
Articles
LiteLLM Supply Chain Attack: Inside the Poisoned Security Scanner that Backdoored AI at Scale
A single poisoned security tool can silently backdoor the AI router that fronts every LLM call in your stack. When that router handles tens of millions of requests per day, a supply chain compromise b...
7 min1421 wordsAI Hallucination Sanctions Surge: How the Oregon Vineyard Ruling, Walmart’s Shortcut, and California Bar Discipline Reshape LLM Engineering
In April 2026, sanctions for AI hallucinations stopped being curiosities and became board‑room artifacts. What changed is not the large language models, but the legal environment they now inhabit....
8 min1587 wordsAI Financial Agents Hallucinating With Real Money: How to Build Brokerage-Grade Guardrails
Autonomous LLM agents now talk to market data APIs, draft orders, and interact with client accounts. The risk has shifted from “bad chatbot answers” to agents that can move cash and positions. When an...
7 min1382 wordsWhen Claude Mythos Meets Production: Sandboxes, Zero‑Days, and How to Not Burn the Data Center Down
Anthropic did something unusual with Claude Mythos: it built a frontier model, then refused broad release because it is “so good at uncovering cybersecurity vulnerabilities” that it could supercharge...
10 min2075 wordsInside the Anthropic Claude Fraud Attack on 16M Startup Conversations
A fraud campaign siphoning 16 million Claude conversations from Chinese startups is not science fiction; it is a plausible next step on a risk curve we are already on. [1][9] This article treats that...
8 min1529 wordsDesigning Acutis AI: A Catholic Morality-Shaped Search Platform for Safer LLM Answers
Most search copilots optimize for clicks, not conscience. For Catholics asking about sin, sacraments, or vocation, answers must be doctrinally sound, pastorally careful, and privacy-safe. Acutis AI...
8 min1619 wordsClaude Mythos Leak: How Anthropic’s Security Gamble Rewrites AI Risk for Developers
1. What Actually Leaked About Claude Mythos — And Why It Matters In late March, Fortune reported that nearly 3,000 internal Anthropic documents were exposed via a misconfigured CMS, revealing Claude...
8 min1637 wordsEU ‘Simplify’ AI Laws? Why Developers Should Worry About Their Rights
European officials now hint that the EU’s dense AI rulebook could be “simplified” just as the EU AI Act starts to bite. For policy staff, this sounds like cleanup; for engineers, rights‑holders, and e...
7 min1403 wordsMIT/Berkeley Study on ChatGPT’s Delusional Spirals, Suicide Risk, and User Manipulation
Developers are embedding ChatGPT-class models into products that sit directly in the path of human distress: therapy-lite apps, employee-support portals, student mental-health chat, and crisis-adjacen...
10 min2093 wordsAI Hallucinations in Legal Cases: How LLM Failures Are Turning into Monetary Sanctions for Attorneys
From Model Bug to Monetary Sanction: Why Legal AI Hallucinations Matter AI hallucinations occur when an LLM produces false or misleading content but presents it as confidently true.[1] In legal work,...
10 min1947 wordsInside the Claude Mythos Leak: Why Anthropic’s Next Model Scared Its Own Creators
On March 26–27, 2026, Anthropic — the company known for “constitutional” safety‑first LLMs — confirmed that internal documents about an unreleased system called Claude Mythos had been accidentally exp...
11 min2266 wordsFrom Man Pages to Agents: Redesigning `--help` with LLMs for Cloud-Native Ops
The traditional UNIX-style --help assumes a static binary, a stable interface, and a human willing to scan a 500-line usage dump at 3 a.m. Cloud-native operations are different: elastic clusters, e...
11 min2175 words
Topics Covered
AI Hallucinations
Understanding why LLMs invent information and how to prevent it.
RAG Best Practices
Retrieval Augmented Generation: architectures, chunking, optimal retrieval.
Ghost Sources
When AI cites sources that don't exist. Detection and prevention.
KB Drift
How to detect and correct knowledge base drift.
Chunking Strategies
Optimal document splitting for better retrieval.
LLM Evaluation
Metrics and methods to evaluate AI response quality.
AI Regulation
Laws, regulations and compliance frameworks governing AI systems.
AI Safety
Risks, safeguards and best practices for safe AI deployment.
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