CVE-2024-0100

NVIDIA Triton Inference Server for Linux contains a vulnerability in the tracing API, where a user can corrupt system files. A successful exploit of this vulnerability might lead to denial of service and data tampering...

CVE-2024-0088

NVIDIA Triton Inference Server for Linux contains a vulnerability in shared memory APIs, where a user can cause an improper memory access issue by a network API. A successful exploit of this vulnerability might lead to denial of service and data tampering...

CVE-2023-31036

NVIDIA Triton Inference Server for Linux and Windows contains a vulnerability where, when it is launched with the non-default command line option --model-control explicit, an attacker may use the model load API to cause a relative path traversal. A successful exploit of this vulnerability may lea...

CVE-2022-21731

Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion. The axis argument is translated into concatdim in the ConcatShapeHelper helper function. Then, a...

CVE-2022-41914

Zulip is an open-source team collaboration tool. For organizations with System for Cross-domain Identity ManagementSCIM account management enabled, Zulip Server 5.0 through 5.6 checked the SCIM bearer token using a comparator that did not run in constant time. Therefore, it might theoretically be...

Towards Anonymous Neural Network Inference

We introduce funion, a system providing end-to-end sender-receiver unlinkability for neural network inference. By leveraging the Pigeonhole storage protocol and BACAP blinding-and-capability scheme from the Echomix anonymity system, funion inherits the provable security guarantees of modern...

An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs

Recent advances in Large Language Models LLMs have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of performing private third-party inference, such as Secure Multiparty Computation SMPC, often rely on cryptographic methods...

CVE-2022-23580

Tensorflow is an Open Source Machine Learning Framework. During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, a...

CVE-2022-20340

In SELinux policy, there is a possible way of inferring which websites are being opened in the browser due to a missing permission check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product:...

CVE-2022-25368

Spectre BHB is a variant of Spectre-v2 in which malicious code uses the shared branch history stored in the CPU BHB to influence mispredicted branches in the victim's hardware context. Speculation caused by these mispredicted branches can then potentially be used to cause cache allocation, which...

CVE-2021-41212

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for tf.ragged.cross can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1,...

CVE-2021-37677

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for tf.rawops.Dequantize has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference implementation use...

ReCopilot: Reverse Engineering Copilot in Binary Analysis

Binary analysis plays a pivotal role in security domains such as malware detection and vulnerability discovery, yet it remains labor-intensive and heavily reliant on expert knowledge. General-purpose large language models LLMs perform well in programming analysis on source code, while...

An Efficient Private GPT Never Autoregressively Decodes

The wide deployment of the generative pre-trained transformer GPT has raised privacy concerns for both clients and servers. While cryptographic primitives can be employed for secure GPT inference to protect the privacy of both parties, they introduce considerable performance overhead.To accelerat...

DynaNoise: Dynamic Probabilistic Noise Injection for Defending against Membership Inference Attacks

Membership Inference Attacks MIAs pose a significant risk to the privacy of training datasets by exploiting subtle differences in model outputs to determine whether a particular data sample was used during training. These attacks can compromise sensitive information, especially in domains such as...

Trend Joins NVIDIA to Secure AI Infrastructure with NVIDIA

Together, we are focused on securing the full AI lifecycle—from development and training to deployment and inference—across cloud, data center, and AI factories...

Automated Profile Inference with Language Model Agents

Impressive progress has been made in automated problem-solving by the collaboration of large language models LLMs based agents. However, these automated capabilities also open avenues for malicious applications. In this paper, we study a new threat that LLMs pose to online pseudonymity, called...

Privacy-Preserving AI for Encrypted Medical Imaging: a Framework for Secure Diagnosis and Learning

The rapid integration of Artificial Intelligence AI into medical diagnostics has raised pressing concerns about patient privacy, especially when sensitive imaging data must be transferred, stored, or processed. In this paper, we propose a novel framework for privacy-preserving diagnostic inferenc...

On Membership Inference Attacks in Knowledge Distillation

Nowadays, Large Language Models LLMs are trained on huge datasets, some including sensitive information. This poses a serious privacy concern because privacy attacks such as Membership Inference Attacks MIAs may detect this sensitive information. While knowledge distillation compresses LLMs into...

Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy

Large Language Models LLMs have gained significant popularity due to their remarkable capabilities in text understanding and generation. However, despite their widespread deployment in inference services such as ChatGPT, concerns about the potential leakage of sensitive user data have arisen...