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Protect your digital life with the latest privacy news, covering smart device surveillance risks and OS security backdoors.
Cloud agents typically send user messages to remote LLMs and store conversation traces in memory systems (e.g., Mem0, LangMem, Memobase) for long-term personalization. This creates a large privacy attack surface: plaintext prompts and logs may contain PII, medical/financial data, credentials cloud memory stores can leak via retrieval, prompt injection, inversion, or misconfiguration naïve mitigation (e.g., *** masking) destroys task semantics, harming retrieval and personalization Goal: reduce privacy leakage without sacrificing utility.
Paper page - MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents…Unveiling Latent Ripple Effects and Shallow Forgetting in Privacy Unlearning (2026) Measuring the Depth of LLM Unlearning via Activation Patching (2026) CiPO: Counterfactual Unlearning for Large Reasoning Models through Iterative Preference Optimization…
…That balance between performance, privacy, and efficiency is something many developers have been waiting for. 👍 💡 The focus on tool calling, instruction following, and multi-step workflows shows that smaller models can still…
…Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills (2026) DynamicMem: A Long-Horizon Memory Benchmark in Real-World Settings (2026) When Role-playing, Do Models Believe What They Say? (2026…
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