LIVE FEED
HIGH OpenAI, Anthropic, Google APIs Let Weaker Models Steal Reasoning // HIGH LLM Reasoning Trace Theft via Encrypted Block Replay Attack // FIRST LOOK OpenAI and AWS Launch Daybreak Red and Blue on Amazon Bedrock // CRITICAL CVE-2026-55040: SharePoint RCE Chain Found via AI Agent // FIRST LOOK OpenAI Releases GPT-5.6 Cyber for Approved Security Partners // HIGH GhostJacking Attack Hijacks AI Agents via Security Alerts // FIRST LOOK Cactus Releases Needle 2 Agentic LLM for IoT and Edge Devices // HIGH Google APK Flaw Enables Agent-to-Agent Supply Chain Attack // CRITICAL CVE-2026-41679: Paperclip AI RCE via Malicious Agent Import // FIRST LOOK Anthropic Enables Claude Code Auto Mode by Default for Pro Users //
OPEN-SOURCE-LLM

Open-Source-Llm

2 reports
All LLM Security Agentic AI Industry News Supply Chain Research Prompt Injection First Look: Security Regulatory Jailbreaks Adversarial ML Model Theft Data Poisoning Deep Signal Intelligence Report AI Governance AI Security AI Security Tools Identity & Access Management Security Operations
AI Widens Skill-Ability Gap, Enabling Autonomous Cyberattacks

AI Widens Skill-Ability Gap, Enabling Autonomous Cyberattacks

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 Schneier on Security

A Five Eyes joint advisory and Bruce Schneier's analysis highlight how AI systems are dramatically lowering the barrier to sophisticated cyberattacks by decoupling skill from ability. Open-source and frontier models can autonomously execute network intrusions, ransomware deployment, and data theft with minimal user expertise. The piece argues that guardrails from major AI vendors are insufficient, as uncensored open-source models circulate freely and continue to improve.

NanoEuler Launches GPT-2 LLM Built from Scratch in C/CUDA

NanoEuler Launches GPT-2 LLM Built from Scratch in C/CUDA

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 5.8 Cohere AI (via HN)

NanoEuler is an open-source GPT-2-class language model (~116M parameters) built entirely from scratch in C/CUDA, including hand-written backpropagation, a BPE tokenizer, FlashAttention, pretraining, and supervised fine-tuning — with RLHF/DPO planned. For defenders, the significance lies in the democratisation of low-level, dependency-free LLM training infrastructure: adversaries gain a highly portable, auditable, and modifiable training stack that bypasses standard ML framework telemetry and supply chain controls. Security teams should treat this class of 'from-scratch' open-source LLM tooling as a potential foundation for covert fine-tuning pipelines, backdoor insertion, and evasion of model-level safety controls.

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