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Positive Technologies
Positive Technologies
added 2025/07/01 12:00 a.m.15 views

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...

5.3CVSS6.2AI score0.00268EPSS
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Debian CVE
Debian CVE
added 2025/06/24 3:21 a.m.8 views

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...

8.8CVSS5.3AI score0.00318EPSS
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Packet Storm News
Packet Storm News
added 2025/06/23 12:00 a.m.8 views

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...

7AI score
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Packet Storm News
Packet Storm News
added 2025/06/22 12:00 a.m.12 views

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...

6.9AI score
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Packet Storm News
Packet Storm News
added 2025/06/22 12:00 a.m.13 views

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...

6.8AI score
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Packet Storm News
Packet Storm News
added 2025/06/22 12:00 a.m.9 views

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...

6.8AI score
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Packet Storm News
Packet Storm News
added 2025/06/22 12:00 a.m.27 views

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...

6.9AI score
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Packet Storm News
Packet Storm News
added 2025/06/22 12:00 a.m.11 views

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...

7.1AI score
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Packet Storm News
Packet Storm News
added 2025/06/21 12:00 a.m.9 views

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...

6.8AI score
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Packet Storm News
Packet Storm News
added 2025/06/21 12:00 a.m.9 views

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...

6.8AI score
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Packet Storm News
Packet Storm News
added 2025/06/21 12:00 a.m.8 views

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...

7AI score
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Packet Storm News
Packet Storm News
added 2025/06/19 12:00 a.m.11 views

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...

6.6AI score
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Packet Storm News
Packet Storm News
added 2025/06/12 12:00 a.m.6 views

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...

7AI score
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Packet Storm News
Packet Storm News
added 2025/06/12 12:00 a.m.9 views

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...

7.1AI score
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Trend Micro Simply Security
Trend Micro Simply Security
added 2025/06/11 12:00 a.m.5 views

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...

7.3AI score
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Packet Storm News
Packet Storm News
added 2025/06/10 12:00 a.m.11 views

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...

6.9AI score
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Packet Storm News
Packet Storm News
added 2025/06/09 12:00 a.m.11 views

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...

6.8AI score
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Packet Storm News
Packet Storm News
added 2025/06/09 12:00 a.m.8 views

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...

6.9AI score
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Packet Storm News
Packet Storm News
added 2025/06/06 12:00 a.m.9 views

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...

7AI score
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Packet Storm News
Packet Storm News
added 2025/06/06 12:00 a.m.10 views

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...

6.9AI score
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