First reported · updated · 5 reports forcepoint.com
Analysis · latest
First reported sciencedirect.com
From AI-generated content to agentic action: Security and safety threats in generative AI
A review paper in the Journal of Information and Intelligence titled 'From AI-generated content to agentic action' surveys the security and safety implications as generative AI systems move from producing content to retrieving data, invoking tools, and executing actions through tool chains and external APIs. It analyzes content-level, model-level, and agentic threats alongside countermeasures such as detection, watermarking, alignment, and emerging agentic safeguards, arguing that attack-surface expansion outpaces defensive responses. Details →First reported · updated · 4 reports arxiv.org
Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies
A survey paper by Kiarash Ahi and Saeed Valizadeh reviews over 70 academic papers, industry reports, and technical documents on the dual-use of LLMs and generative AI in cybersecurity, covering AI-generated malware, zero-day detection, explainable AI, and defensive strategies. Drawing on case studies from platforms like Google Play Protect, Microsoft Defender, and Hugging Face, it offers recommendations including model watermarking, adversarial defense, and cross-industry collaboration. Details →First reported dailyjus.com
Prompt Injection: Are Invisible Instructions the Next AI Risk in Disputes? – Daily Jus by Jus Mundi
Legal analysts at Greenberg Traurig examine prompt injection as an emerging AI risk in legal disputes, describing how hidden instructions embedded in documents can manipulate AI tools that ingest them into producing skewed or incomplete outputs. The piece contrasts this with AI hallucinations and notes a court has already dealt with the issue. Details →First reported amazon.com
Designing for the inevitable: System prompt leakage and mitigations in generative AI applications | AWS Security Blog
An AWS Security Blog post titled "Designing for the inevitable: System prompt leakage and mitigations in generative AI applications" discusses the risk of system prompt leakage in LLM-based applications and offers guidance on mitigations, referencing the OWASP Top 10 for LLM Applications (LLM07: System Prompt Leakage). Details →How the wire is made
Poll & cluster
Internet is crawled for AI security news and near-duplicate coverage is embedded and grouped into durable items.
Curate
AI Agent filters for agentic-AI relevance, classifies and tags each item, scores severity for threats, and writes the summary.
Every item here is one machine-curated intelligence object, not a headline.
Read the wire for free. There is a small charge to ask the index questions.
The wire, open
The complete curated feed, no key required.
- GET /feed.xml — RSS 2.0, every item
- GET /api/items — read-only
The vector desk
Query the index by meaning, not just keyword.
- GET /api/items?tags=&minSeverity=&itemType=
- GET /api/search?q= — keyword
- GET /api/semantic?q= — vector