1481 matches found
PT-2025-27527 · Unknown · Ai-Inference-Server
Name of the Vulnerable Software and Affected Versions: ai-inference-server affected versions not specified Description: A flaw was found in the authentication enforcement mechanism of a model inference API. The issue affects the "/v1/" endpoints, where API key validation is expected but not...
CVE-2025-52566
llama.cpp is an inference of several LLM models in C/C++. Prior to version b5721, there is a signed vs. unsigned integer overflow in llama.cpp's tokenizer implementation llamavocab::tokenize src/llama-vocab.cpp:3036 resulting in unintended behavior in tokens copying size comparison. Allowing...
Amplifying Machine Learning Attacks through Strategic Compositions
Machine learning ML models are proving to be vulnerable to a variety of attacks that allow the adversary to learn sensitive information, cause mispredictions, and more. While these attacks have been extensively studied, current research predominantly focuses on analyzing each attack type...
UCD: Unlearning in LLMs Via Contrastive Decoding
Machine unlearning aims to remove specific information, e.g. sensitive or undesirable content, from large language models LLMs while preserving overall performance. We propose an inference-time unlearning algorithm that uses contrastive decoding, leveraging two auxiliary smaller models, one train...
Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models
Large vision-language models LVLMs have demonstrated outstanding performance in many downstream tasks. However, LVLMs are trained on large-scale datasets, which can pose privacy risks if training images contain sensitive information. Therefore, it is important to detect whether an image is used t...
SecONNds: Secure Outsourced Neural Network Inference on ImageNet
The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing frameworks suffer from high computational overhead and...
HE-LRM: Encrypted Deep Learning Recommendation Models Using Fully Homomorphic Encryption
Fully Homomorphic Encryption FHE is an encryption scheme that not only encrypts data but also allows for computations to be applied directly on the encrypted data. While computationally expensive, FHE can enable privacy-preserving neural inference in the client-server setting: a client encrypts...
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: a New Inference Attack Perspective
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing...
Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference
Differential privacy DP auditing aims to provide empirical lower bounds on the privacy guarantees of DP mechanisms like DP-SGD. While some existing techniques require many training runs that are prohibitively costly, recent work introduces one-run auditing approaches that effectively audit DP-SGD...
Don't Throw the Baby out with the Bathwater: How and Why Deep Learning for ARC
The Abstraction and Reasoning Corpus ARC-AGI presents a formidable challenge for AI systems. Despite the typically low performance on ARC, the deep learning paradigm remains the most effective known strategy for generating skillful state-of-the-art neural networks NN across varied modalities and...
ReDASH: Fast and efficient Scaling in Arithmetic Garbled Circuits for Secure Outsourced Inference
Whitepaper called ReDASH: Fast and efficient Scaling in Arithmetic Garbled Circuits for Secure Outsourced Inference...
Black-Box Privacy Attacks on Shared Representations in Multitask Learning
Multitask learning MTL has emerged as a powerful paradigm that leverages similarities among multiple learning tasks, each with insufficient samples to train a standalone model, to solve them simultaneously while minimizing data sharing across users and organizations. MTL typically accomplishes th...
SOFT: Selective Data Obfuscation for Protecting LLM Fine-Tuning against Membership Inference Attacks
Whitepaper called SOFT: Selective Data Obfuscation For Protecting LLM Fine-Tuning Against Membership Inference Attacks...
ObfusBFA: a Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks
Bit-flip attacks BFAs represent a serious threat to Deep Neural Networks DNNs, where flipping a small number of bits in the model parameters or binary code can significantly degrade the model accuracy or mislead the model prediction in a desired way. Existing defenses exclusively focus on...
Enabling Secure AI Inference: Trend Cybertron Leverages NVIDIA Universal LLM NIM Microservices
Learn how Trend's Cybertron has been harnessing the power of NVIDIA Universal LLM NIM Microservices...
ZTaint-Havoc: from Havoc Mode to Zero-Execution Fuzzing-Driven Taint Inference
Fuzzing is a widely used technique for discovering software vulnerabilities, but identifying hot bytes that influence program behavior remains challenging. Traditional taint analysis can track such bytes white-box, but suffers from scalability issue. Fuzzing-Driven Taint Inference FTI offers a...
Doxing Via the Lens: Revealing Location-Related Privacy Leakage on Multi-Modal Large Reasoning Models
Recent advances in multi-modal large reasoning models MLRMs have shown significant ability to interpret complex visual content. While these models enable impressive reasoning capabilities, they also introduce novel and underexplored privacy risks. In this paper, we identify a novel category of...
GradEscape: a Gradient-Based Evader against AI-Generated Text Detectors
In this paper, we introduce GradEscape, the first gradient-based evader designed to attack AI-generated text AIGT detectors. GradEscape overcomes the undifferentiable computation problem, caused by the discrete nature of text, by introducing a novel approach to construct weighted embeddings for t...
Saffron-1: Towards an Inference Scaling Paradigm for LLM Safety Assurance
Existing safety assurance research has primarily focused on training-phase alignment to instill safe behaviors into LLMs. However, recent studies have exposed these methods' susceptibility to diverse jailbreak attacks. Concurrently, inference scaling has significantly advanced LLM reasoning...
Membership Inference Attacks for Unseen Classes
Shadow model attacks are the state-of-the-art approach for membership inference attacks on machine learning models. However, these attacks typically assume an adversary has access to a background nonmember data distribution that matches the distribution the target model was trained on. We initiat...