AMD Processors 安全漏洞

AMD Processors is a processor from Ultraviolet Semiconductor AMD. A security vulnerability exists in AMD Processors that originates from a user process that may speculatively infer control registers, potentially leading to information disclosure...

AMD Processors 安全漏洞

AMD Processors is a processor from Ultraviolet Semiconductor AMD. AMD Processors suffers from a security vulnerability that stems from the possibility that an attacker could infer previously stored data, potentially leading to the disclosure of privileged information...

Moderate: Red Hat Security Advisory: Red Hat AI Inference Server 3.0 (CUDA)

Red Hat AI Inference Server 3.0 CUDA is now available. Red Hat® AI Inference Server...

Moderate: Red Hat Security Advisory: Red Hat AI Inference Server 3.0 (ROCm)

Red Hat AI Inference Server 3.0 ROCm is now available. Red Hat® AI Inference Server...

Cascade: Token-Sharded Private LLM Inference

As LLMs continue to increase in parameter size, the computational resources required to run them are available to fewer parties. Therefore, third-party inference services -- where LLMs are hosted by third parties with significant computational resources -- are becoming increasingly popular...

CVE-2025-6920

A flaw was found in the authentication enforcement mechanism of a model inference API in ai-inference-server. All /v1/ endpoints are expected to enforce API key validation. However, the POST /invocations endpoint failed to do so, resulting in an authentication bypass. This vulnerability allows...

CVE-2025-6920

CVE-2025-6920 affects ai-inference-server: the POST /invocations endpoint bypasses API key validation, permitting unauthorized access to inference features and potentially backend resources. Affected: model inference API under /v1/*; root cause: authentication enforcement failure on /invocations....

CVE-2025-6920 Ai-inference-server: authentication bypass via unprotected inference endpoint in api

A flaw was found in the authentication enforcement mechanism of a model inference API in ai-inference-server. All /v1/ endpoints are expected to enforce API key validation. However, the POST /invocations endpoint failed to do so, resulting in an authentication bypass. This vulnerability allows...

CVE-2025-6920 Ai-inference-server: authentication bypass via unprotected inference endpoint in api

A flaw was found in the authentication enforcement mechanism of a model inference API in ai-inference-server. All /v1/ endpoints are expected to enforce API key validation. However, the POST /invocations endpoint failed to do so, resulting in an authentication bypass. This vulnerability allows...

CVE-2025-6920: Missing Authentication for Critical Function

A flaw was found in the authentication enforcement mechanism of a model inference API in ai-inference-server. All /v1/ endpoints are expected to enforce API key validation. However, the POST /invocations endpoint failed to do so, resulting in an authentication bypass. This vulnerability allows...

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...

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...

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...

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...

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...

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...