From conversational backdoors to fragile authorization decisions and open-weight models with reduced security capabilities, trust in local LLM output is under pressure. Fact-checking pipelines aim to anchor generation to verified verdicts.
A Reddit proposal suggests distributing only abliteration LoRAs instead of full uncensored LLM versions, cutting duplication and costs. The technical point: one adapter works across every model of the same architecture and every quantization. For sel...
Kimi K3.1 and Tencent Hy4 remove cybersecurity, reverse engineering, and patching capabilities from open-weight models. If local models cannot analyze vulnerabilities or binaries, auditing on your own hardware becomes impractical and teams fall back ...
An attack framework uses poisoned dialogue data to steer multi-turn interactions after a trigger, without modifying prompts at deployment time. Three variants — Direct, Constrained Progressive, Unconstrained Progressive — show that models preserve ap...
A team presented a four-stage pipeline that generates fact-checking articles from a claim, the original verdict and web-scraped sources. Llama-3.1-8B-Instruct produces the text, while deterministic post-processing recovers citations. Mean score 0.428...
A benchmark on 3,000 predictions from ten frozen open models shows 86.1% appear plausible, but authorization remains fragile: low ASK recall, strong BLOCK bias. SafeWebWorld and LAR separate evidence preservation from decision calibration. For local ...
