Discourse - Private Group Membership Inference via exclude_groups

Discourse 2026.3.0-latest.1, 2026.2.1, and 2026.1.2 contains an information disclosure caused by unauthenticated users observing directory results changes with the excludegroups parameter, letting attackers determine private group membership, exploit requires public access to user directory. id:...

Priv-QDNN

Priv-QDNN This repository contains a research codebase for privacy evaluation of quantized deep neural networks. It supports training target/shadow models under common post-training quantization PTQ and quantization-aware training QAT settings, saving intermediate attack features to disk, and...

PrivacyRaven

Note: This project is on hiatus. PrivacyRaven is a privacy testing library for deep learning systems. You can use it to determine the susceptibility of a model to different privacy attacks; evaluate privacy preserving machine learning techniques; develop novel privacy metrics and attacks; and...

Automated Screening of Privacy Risks in Medical AI

Medical artificial intelligence AI models are often shared without their training data, assuming this protects the patients who contributed them. However, a model can reveal whether a patient's records were used in training, a privacy breach known as a membership inference attack. This risk is...

Membership Inference Attack on Private Evolution

Synthetic Data Generation SDG is an actively studied way to enable safe sharing of private data more broadly. Inference-only SDG methods, such as Private Evolution PE, are particularly appealing, as they can leverage black-box access to pre-trained foundation models to generate high quality data...

COMPASS: Membership Inference Attack against Vision Large Language Models Via Adaptive Noise Calibration

Vision large language models VLLMs are deployed in privacy-sensitive domains, raising training-data exposure concerns. Existing gray-box membership inference attacks MIAs commonly rely on entropy statistics or generated descriptions, requiring costly generation with limited membership separation...

Power Side-Channel Membership Inference Attack on Embedded Machine Learning

Membership inference attacks MIAs threaten the privacy of machine learning ML training data by determining whether a sample was used to train a target model. Existing MIAs rely on model outputs, ranging from prediction probabilities to predicted labels, an assumption that can be restrictive for...

Hidden Risks of Jev: An Empirical Study of Security, Privacy, and Dual Use

Jev turns natural-language questions into typed answers and probabilities with low latency and cost, enabling applications to route requests and select tools. While this interface allows Jev to integrate naturally into application workflows as a decision layer, the security and privacy implicatio...

Leaky Students: Membership Inference against On-Policy Distillation

On-policy distillation OPD trains a student to match a teacher's next-token distributions on student-generated trajectories. However, privileged information supplied to the teacher for OPD training may contain sensitive data. Whether the student leaks private information about the records supplie...

Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models

Text-to-Speech TTS foundation models are increasingly fine-tuned on private datasets to synthesize highly personalized voices, introducing severe privacy risks by exposing both biometric identities and sensitive speech content. Existing black-box membership inference attacks MIAs follow a two-sta...

Mitigating Explanation Leakage in Financial Fraud Detection Systems

Financial fraud detection relies heavily on centralized machine learning models. This creates serious data privacy risks. Federated Learning FL decentralizes data processing, but financial regulations still require models to be transparent. This means using Explainable AI XAI tools such as...

Evaluating Frontier AI Agents As Autonomous Clinical Security Auditors

Clinical AI models can expose patients to harm when adversarial vulnerabilities go undetected, yet formal security auditing requires statistical expertise, specialized tools, and significant time. We present an open evaluation task, built on METR Task Standard v0.3.0, that tests whether frontier ...

On Reliability of Efficient Membership Inference Vulnerability Evaluation

Membership inference attacks MIAs are popular methods for empirically assessing the leakage of sensitive information in the training data through models or statistics learned from the data. The MIA vulnerability is often evaluated through false positive rate FPR and true positive rate TPR of a...

AI Security Research Should Better Incentivize Defense Research

This work examines an imbalance in artificial intelligence AI security research: the field tends to produce more work on attacking AI systems than on defending them. Drawing on related academic papers, we find biased attack-to-defense ratios across subfields, including federated learning, speech...

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications

Large Language Models LLMs have become the predominant paradigm in NLP, advancing both research and industry. As model sizes and pretraining data grow, concerns about Pretraining Data Exposure PDE increase due to the scale and opacity of training datasets. PDE refers to determining whether specif...

Evaluating Differential Privacy against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge

While Federated Learning FL mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks MIAs. This paper presents an empirical evaluation of Differential Privacy DP as a defense mechanism against MIAs in FL, leveraging the environment of the 202...

EUVD-2026-17555

Discourse is an open-source discussion platform. From versions 2026.1.0-latest to before 2026.1.3, 2026.2.0-latest to before 2026.2.2, and 2026.3.0-latest to before 2026.3.0, there is possible channel membership inference from chat user search without authorization. This issue has been patched in...

CVE-2026-32618

Discourse (open-source discussion platform) has a channel membership inference vulnerability affecting versions 2026.1.0-latest to before 2026.1.3, 2026.2.0-latest to before 2026.2.2, and 2026.3.0-latest to before 2026.3.0, where chat user search could reveal channel membership without authorizat...

CVE-2026-32618: Exposure of Sensitive Information to an Unauthorized Actor

Discourse is an open-source discussion platform. From versions 2026.1.0-latest to before 2026.1.3, 2026.2.0-latest to before 2026.2.2, and 2026.3.0-latest to before 2026.3.0, there is possible channel membership inference from chat user search without authorization. This issue has been patched in...

Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks

Retrieval-Augmented Generation RAG significantly mitigates the hallucinations and domain knowledge deficiency in large language models by incorporating external knowledge bases. However, the multi-module architecture of RAG introduces complex system-level security vulnerabilities. Guided by the R...