An incident and vulnerability tracker for AI/ML systems. Model leaks, training-data exposures, jailbreak disclosures, ML library CVEs, vendor breaches, and confirmed prompt-injection-in-the-wild — each entry linked to a primary source, dated, and tagged for filtering.
RAG systems inherit all the vulnerabilities of LLMs and add a new one: the retrieval corpus. Injecting malicious content into retrieved sources can hijack model behavior in ways users and operators don't see coming.
A documented review of security incidents involving autonomous AI agents in 2024-2025, covering tool misuse, privilege escalation via injection, and the architectural patterns that created the exposure.
Crafting a convincing, personalized phishing email once required hours of research per target. Large language models have reduced that cost to seconds. This post examines the economics of AI-assisted phishing, 2024-2025 incident data, and what defenders can measure.
Enterprise LLM deployments are being red-teamed at scale for the first time. Security practitioners find consistent failure patterns — misconfigured system prompts, inadequate output filtering, and agentic privilege escalation paths operators didn't anticipate.
AI incidents and vulnerabilities — tracked, sourced, dated. — delivered when there's something worth your inbox.
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