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 //
FINE-TUNING

Fine-Tuning

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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.

Anthropic CEO: Open-Source AI Models Pose Systemic Safety Risk

Anthropic CEO: Open-Source AI Models Pose Systemic Safety Risk

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 6.2 Meta AI (via HN)

Anthropic CEO Dario Amodei testified to lawmakers that open-source AI models present a systemic safety risk because once released, developers lose the ability to monitor misuse, revoke access, or patch safety guardrails. For defenders, this formalises a long-standing asymmetry: closed-source safety controls (rate-limiting, usage monitoring, kill-switches) become irrelevant once capable weights are publicly distributed. Security teams building on or competing against open-weight models must now treat every downloaded model artifact as a potentially unpatched, unmonitored endpoint that can be fine-tuned to remove safety constraints entirely.

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