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MODEL-WEIGHTS

Model-Weights

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Lazarus Attack Backdoors AI Agents via Under 10 Neurons

Lazarus Attack Backdoors AI Agents via Under 10 Neurons

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 The Hacker News

Microsoft researchers have demonstrated a technique called Lazarus that implants a hidden backdoor into open-weight LLMs by modifying fewer than 10 rarely-active neurons, requiring no model retraining. When a predefined trigger string appears in input or tool output, the backdoored model injects an attacker-controlled command into its output, which an AI agent then executes with its own permissions. The attack preserves normal model behaviour and benchmark performance, making detection extremely difficult, and poses a serious supply chain risk for any organisation deploying open-weight models.

Moonshot AI Releases Kimi K3 Open-Weight 2.8T Model Weights

Moonshot AI Releases Kimi K3 Open-Weight 2.8T Model Weights

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 5.8 Simon Willison

Moonshot AI has released the weights for Kimi K3, a 2.8 trillion parameter mixture-of-experts model (1.56TB), distributed under a restrictive 'open weight' licence that requires a separate commercial agreement for large MaaS operators. The public availability of weights at this scale materially lowers the barrier for adversarial fine-tuning, jailbreak research, and model-theft-adjacent supply chain attacks. Defenders deploying or downstream of K3 should assess licence compliance risk alongside the standard open-weight threat model.

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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