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
Meta’s AI Model Delay: What It Means for Developers, Security, and Production Roadmaps
Meta’s decision to delay the developer release of its newest AI model reflects a market where expectations for foundation models and broader Foundation Systems have shifted. Regulators enforce transpa...
8 min1597 wordsHow BadHost Auth Bypass in Starlette Can Expose Your AI Agents
When a Starlette app trusts the Host header for authentication or tenant routing, a basic web bug turns into an agentic control‑plane vulnerability. If that service fronts AI agents with tool access,...
5 min964 wordsTrump’s New AI Executive Order: What Early Federal Access to Models Would Mean for ML Engineering
Trump’s AI agenda treats “winning the AI race” as a geopolitical and economic necessity, prioritizing national and economic security over precautionary regulation. [1][9][10] A likely next step is...
7 min1442 wordsHow a Meta AI Support Bot Could Be Hijacked to Steal Instagram Accounts via Prompt Injection
An AI “support assistant” that can reset passwords, change recovery settings, and call internal Meta APIs is effectively a remote admin console behind a chat UI. When this console is driven by an LLM,...
11 min2245 wordsInside the Meta AI Support Bot Prompt Injection Hack: How Attackers Hijacked High-Profile Instagram Accounts
A fake “Meta Support” chat plus a few crafted messages is now enough to compromise accounts worth millions in brand equity. In late 2025 and early 2026, creators reported losing control of high-fol...
10 min2038 wordsInside Sysdig’s First Documented LLM-Agent-Driven Cyber Intrusion: An Engineering Playbook
LLM agents just crossed a line. Sysdig’s report of what appears to be the first documented LLM‑agent‑driven intrusion shows an AI system not only assisting an attacker, but orchestrating an end‑to‑end...
11 min2215 wordsInside the First LLM-Agent-Driven Cyber Intrusion: How an AI Operator Exfiltrated a Database in Under an Hour
An AI agent driven by large language models (LLMs), armed with VPN credentials and access to an internal AI assistant, is now a realistic intruder. Research already shows assistants can be hijacked as...
12 min2358 wordsMay 2026 Enterprise AI Hallucination Crisis: How Automated Workflows Broke and How to Fix Them
In May 2026, several Fortune 500s saw the same pattern: - Accounts‑receivable bots sent thousands of wrong invoices - Ticket routers pushed urgent complaints to the wrong regions - Compliance ag...
11 min2241 wordsDesigning with MiniMax M3: Architecting Long‑Context AI Coding Systems That Actually Ship
Long-context code models promise repo-level generation and multi-day refactors, but most agents still fail on real projects unless the surrounding system is carefully engineered. Frontier code mode...
7 min1498 wordsClawHavoc Exposed: How 824 Malicious LLM Skills Infected the OpenClaw Marketplace
824 “skills” turned a trusted marketplace for large language models into an adversarial toolchain, quietly riding on verified badges and production AI agents.[9] ClawHavoc shows how one compromised ma...
10 min2032 wordsOWASP GenAI Q1 2026 Exploit Round-up: From Flowise RCE to Claude-Assisted Breaches
1. Why GenAI Exploits Are Accelerating in 2026 OWASP’s LLM Top 10 treats GenAI as a distinct attack surface, not “just another API.”[1] It formalizes risks such as prompt injection, data leakage, ina...
10 min1932 wordsHow an AI Coding Agent Triggered a Recursive Deletion Disaster in May 2026 (and How to Architect for Failure Containment)
In May 2026, two incidents made clear that AI coding agents are no longer “IDE assistants” but autonomous actors capable of destroying production systems at machine speed. - At PocketOS, a Claude Opu...
11 min2224 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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