CVE-2024-51977

An unauthenticated attacker who can access either the HTTP service TCP port 80, the HTTPS service TCP port 443, or the IPP service TCP port 631, can leak several pieces of sensitive information from a vulnerable device. The URI path /etc/mntinfo.csv can be accessed via a GET request and no...

Adversarial Threats in Quantum Machine Learning: a Survey of Attacks and Defenses

Quantum Machine Learning QML integrates quantum computing with classical machine learning, primarily to solve classification, regression and generative tasks. However, its rapid development raises critical security challenges in the Noisy Intermediate-Scale Quantum NISQ era. This chapter examines...

CVE-2024-51977

An unauthenticated attacker who can access either the HTTP service TCP port 80, the HTTPS service TCP port 443, or the IPP service TCP port 631, can leak several pieces of sensitive information from a vulnerable device. The URI path /etc/mntinfo.csv can be accessed via a GET request and no...

CVE-2024-51977

An unauthenticated attacker who can access either the HTTP service TCP port 80, the HTTPS service TCP port 443, or the IPP service TCP port 631, can leak several pieces of sensitive information from a vulnerable device. The URI path /etc/mntinfo.csv can be accessed via a GET request and no...

CVE-2024-51977

CVE-2024-51977 affects at least one Brother multi‑function device (notably the MFC‑L9570CDW) where an unauthenticated attacker can reach the HTTP/HTTPS/IPP services on ports 80/443/631 and retrieve /etc/mnt_info.csv. The CSV exposes device info including model, firmware version, IP address, and s...

CVE-2024-51977 Unauthenticated leak of sensitive information affecting multiple models from Brother Industries, Ltd., FUJIFILM Business Innovation, Ricoh, Toshiba Tec, and Konica Minolta, Inc.

An unauthenticated attacker who can access either the HTTP service TCP port 80, the HTTPS service TCP port 443, or the IPP service TCP port 631, can leak several pieces of sensitive information from a vulnerable device. The URI path /etc/mntinfo.csv can be accessed via a GET request and no...

Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers

Deep Neural Networks DNNs are notoriously vulnerable to adversarial input designs with limited noise budgets. While numerous successful attacks with subtle modifications to original input have been proposed, defense techniques against these attacks are relatively understudied. Existing defense...

Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA

Memorization in large language models LLMs makes them vulnerable to data extraction attacks. While pre-training memorization has been extensively studied, fewer works have explored its impact in fine-tuning, particularly for LoRA fine-tuning, a widely adopted parameter-efficient method. In this...

ZKPROV: a Zero-Knowledge Approach to Dataset Provenance for Large Language Models

As the deployment of large language models LLMs grows in sensitive domains, ensuring the integrity of their computational provenance becomes a critical challenge, particularly in regulated sectors such as healthcare, where strict requirements are applied in dataset usage. We introduce ZKPROV, a...

JsDeObsBench: Measuring and Benchmarking LLMs for JavaScript Deobfuscation

Deobfuscating JavaScript JS code poses a significant challenge in web security, particularly as obfuscation techniques are frequently used to conceal malicious activities within scripts. While Large Language Models LLMs have recently shown promise in automating the deobfuscation process,...

Assessing Risk of Stealing Proprietary Models for Medical Imaging Tasks

The success of deep learning in medical imaging applications has led several companies to deploy proprietary models in diagnostic workflows, offering monetized services. Even though model weights are hidden to protect the intellectual property of the service provider, these models are exposed to...

Diffusion-Based Task-Oriented Semantic Communications with Model Inversion Attack

Semantic communication has emerged as a promising neural network-based system design for 6G networks. Task-oriented semantic communication is a novel paradigm whose core goal is to efficiently complete specific tasks by transmitting semantic information, optimizing communication efficiency and ta...

llama.cpp 安全漏洞

llama.cpp is a multimodal model by the individual developer Georgi Gerganov. A security vulnerability exists in versions of llama.cpp prior to b5721, which stems from the presence of signed and unsigned integer overflows in the tokenizer implementation, which could lead to a heap overflow...

FuncVul: an Effective Function Level Vulnerability Detection Model Using LLM and Code Chunk

Software supply chain vulnerabilities arise when attackers exploit weaknesses by injecting vulnerable code into widely used packages or libraries within software repositories. While most existing approaches focus on identifying vulnerable packages or libraries, they often overlook the specific...

Hitachi Relion 安全漏洞

Hitachi Relion is used by Hitachi, Ltd. of Japan to protect, control, measure, and monitor for power systems. A security vulnerability exists in Hitachi Relion that stems from improper disk space management and may cause the device to reboot. The following models are affected: 670, 650 and...

Recalling the Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy

Machine Unlearning MU technology facilitates the removal of the influence of specific data instances from trained models on request. Despite rapid advancements in MU technology, its vulnerabilities are still under explored, posing potential risks of privacy breaches through leaks of ostensibly...

RepuNet: a Reputation System for Mitigating Malicious Clients in DFL

Decentralized Federated Learning DFL enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects peers for model aggregation. Malicious nodes may exploit this autonomy by sending corrupted models model poisoning,...

Decompiling Smart Contracts with a Large Language Model

The widespread lack of broad source code verification on blockchain explorers such as Etherscan, where despite 78,047,845 smart contracts deployed on Ethereum as of May 26, 2025, a mere 767,520 1% are open source, presents a severe impediment to blockchain security. This opacity necessitates the...

CLSA-2025-1750688636 gcc: Fix of CVE-2020-11023

CVE-2020-11023: sanitize HTML content passed to DOM manipulation methods to prevent execution of untrusted code...

Google Adds Multi-Layered Defenses to Secure GenAI from Prompt Injection Attacks

Google has revealed the various safety measures that are being incorporated into its generative artificial intelligence AI systems to mitigate emerging attack vectors like indirect prompt injections and improve the overall security posture for agentic AI systems. "Unlike direct prompt injections,...