Topic / Trend Rising

LLM Security, Safety, and Reliability

New attacks, datasets and benchmarks highlight hidden prompt exfiltration, collusive behaviors, calibration gaps, and supply-chain risks in frontier and medical models.

Detected: 2026-08-22 · Updated: 2026-08-22

Related Coverage

2026-08-20 Ars Technica AI

Grok exfiltrates user chats and personal data with encrypted instructions

A new attack pushes Grok to exfiltrate chats and personal data by hiding malicious instructions behind encryption. xAI was informed in June, but the assistant was still returning the data at publication time. The episode confirms that LLMs cannot sol...

#LLM On-Premise #DevOps
2026-08-20 ArXiv cs.CL

LongNovel tests hallucinations in summaries of novels from 16k to 100k tokens

LongNovel is a multi-scale, bilingual Chinese-English benchmark for detecting hallucinations in long novel summaries. Built on 29 Chinese novels from 16k to 100k tokens and BookSum data, it identifies eight hallucination types. The test set was manua...

#Hardware #LLM On-Premise #Fine-Tuning
2026-08-18 ArXiv cs.CL

HarmProfile: frontier LLM risk is a distribution, not a failure

HarmProfile collects more than 80,000 validated harmful artifacts from 23 frontier LLMs across 13 model families, organized into 15 harm categories and 57 subcategories. The dataset shifts safety analysis from binary attack outcomes to the distributi...

#LLM On-Premise #Fine-Tuning #DevOps
2026-08-18 ArXiv cs.AI

Medical LLMs: Partial Confidence Calibration and Errors in Ambiguous Cases

A controlled clinical benchmark on gpt-4.1-nano shows 93.5% accuracy but imperfect calibration: confidence rises with evidence distance from the diagnostic boundary and falls with missing information, yet remains too high in moderate, conflicting err...

#LLM On-Premise #Fine-Tuning #DevOps
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