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3 posts

• Cross-Tenant Data Leakage in Multi-Tenant LLM Deployments: Incidents, Architecture, and What to Demand from Providers

  When an LLM service hosts multiple customers on shared infrastructure, failures in conversation isolation, prompt cache sharing, or conversation history storage can expose one customer's data to another. This post covers the documented failure modes, the March 2023 ChatGPT Redis incident, batch inference and system prompt bleed risks, and what enterprise architects should demand from LLM providers before trusting them with sensitive data.

  Jul 15, 2026 llm-security multi-tenant data-leakage inference-infrastructure privacy prompt-caching gdpr enterprise-security defense-patterns

• Membership Inference Attacks: Detecting What Was in an AI Model's Training Data

  Membership inference attacks let adversaries determine—with significant accuracy—whether a specific data record was used to train a model. This post covers the attack mechanisms, why models leak this information, real-world threat scenarios, and how differential privacy and machine unlearning address (and fail to fully close) the gap.

  Jul 5, 2026 privacy membership-inference differential-privacy machine-unlearning attack-defense gdpr overfitting

• Machine Unlearning Security: When Forgetting Training Data Creates New Vulnerabilities

  Regulators are demanding AI systems forget specific training data. Attackers are figuring out how to weaponize that forgetting — through verification gaps, induced forgetting abuse, and unlearning as a path to alignment degradation.

  Jul 2, 2026 machine-unlearning privacy gdpr safety-alignment adversarial-ml model-security membership-inference