This article experimentally evaluates a privacy‑preserving LLM security pipeline that discards raw dialogue after extracting 28 telemetry features, showing that using only 11 text‑independent signals retains about 98.5% of detection performance while reducing false‑positive rates.
The article presents a privacy‑first system that extracts numeric telemetry from each LLM interaction, discards raw text, and evaluates whether detection of prompt injection and jailbreak attacks remains effective, showing only a 1.4 F1‑point drop when using solely text‑independent features.
This article presents HiddenDetect, a training‑free method that leverages refusal‑semantic vectors and layer‑wise activation analysis to detect jailbreak attempts in multimodal large language models, revealing distinct safety signals across text and image modalities and demonstrating strong performance on several LVLM benchmarks.
The paper reveals that large multimodal language models retain refusal signals in their hidden states even after jailbreak attempts, and proposes a training‑free detection method that leverages these signals to identify unsafe inputs across text and image modalities with strong generalization.
This article explains the principles and practical implementations of iOS security hardening techniques—including code obfuscation, anti‑debugging, signature verification, and abnormal data detection—illustrated with real‑world examples from the 58.com iOS client.
