Vulnerability of software for modeling, designing, and drawing in AutoCAD, related to the execution of operations beyond buffer boundaries in memory, allowing attackers to execute arbitrary code or cause system failures.

The vulnerability of software for modeling, designing, and drawing in AutoCAD is related to the execution of operations beyond the buffer boundaries in memory. Exploiting this vulnerability can allow an attacker to execute arbitrary code or cause a service failure using a specially created 3DM fi...

Cryptanalysis of LC-MUME: a Lightweight Certificateless Multi-User Matchmaking Encryption for Mobile Devices

Yang et al. proposed a lightweight certificateless multiuser matchmaking encryption LC-MUME scheme for mobile devices, published in IEEE Transactions on Information Forensics and Security TIFS DOI: 10.1109/TIFS.2023.3321961. Their construction aims to reduce computational and communication overhe...

Resource-Efficient Automatic Software Vulnerability Assessment Via Knowledge Distillation and Particle Swarm Optimization

The increasing complexity of software systems has led to a surge in cybersecurity vulnerabilities, necessitating efficient and scalable solutions for vulnerability assessment. However, the deployment of large pre-trained models in real-world scenarios is hindered by their substantial computationa...

CVE-2025-7675

A maliciously crafted 3DM file, when parsed through certain Autodesk products, can force an Out-of-Bounds Write vulnerability. A malicious actor may leverage this vulnerability to cause a crash, cause data corruption, or execute arbitrary code in the context of the current process...

CVE-2025-5043

A maliciously crafted 3DM file, when linked or imported into certain Autodesk products, can force a Heap-Based Overflow vulnerability. A malicious actor can leverage this vulnerability to cause a crash, read sensitive data, or execute arbitrary code in the context of the current process...

CVE-2025-7675

A maliciously crafted 3DM file, when parsed through certain Autodesk products, can force an Out-of-Bounds Write vulnerability. A malicious actor may leverage this vulnerability to cause a crash, cause data corruption, or execute arbitrary code in the context of the current process...

CVE-2025-5043

A maliciously crafted 3DM file, when linked or imported into certain Autodesk products, can force a Heap-Based Overflow vulnerability. A malicious actor can leverage this vulnerability to cause a crash, read sensitive data, or execute arbitrary code in the context of the current process...

Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems

The increasing digitization of smart grids has improved operational efficiency but also introduced new cybersecurity vulnerabilities, such as False Data Injection Attacks FDIAs targeting Automatic Generation Control AGC systems. While machine learning ML and deep learning DL models have shown...

POLARIS: Explainable Artificial Intelligence for Mitigating Power Side-Channel Leakage

Microelectronic systems are widely used in many sensitive applications e.g., manufacturing, energy, defense. These systems increasingly handle sensitive data e.g., encryption key and are vulnerable to diverse threats, such as, power side-channel attacks, which infer sensitive data through dynamic...

Securing Cloud AI and LLMs with TotalAI for Visibility, Risk Context and Control

As enterprises accelerate AI adoption, large language models LLMs hosted on public cloud platforms are quickly becoming the norm due to their simplified access and pricing model. Cloud-native services like AWS Bedrock, Azure AI Foundry, and Google Vertex AI offer powerful, pay-as-you-go access to...

CVE-2025-54412

A flaw was found in skops. An inconsistency in OperatorFuncNode can hide the execution of untrusted operator methods when a specially crafted model file is loaded. This issue allows arbitrary code execution at load time...

Hot-Swap MarkBoard: an Efficient Black-Box Watermarking Approach for Large-Scale Model Distribution

Recently, Deep Learning DL models have been increasingly deployed on end-user devices as On-Device AI, offering improved efficiency and privacy. However, this deployment trend poses more serious Intellectual Property IP risks, as models are distributed on numerous local devices, making them...

Enhancing Jailbreak Attacks on LLMs Via Persona Prompts

Jailbreak attacks aim to exploit large language models LLMs by inducing them to generate harmful content, thereby revealing their vulnerabilities. Understanding and addressing these attacks is crucial for advancing the field of LLM safety. Previous jailbreak approaches have mainly focused on dire...

Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Features

The rapid advancement of deepfake and face swap technologies has raised significant concerns in digital security, particularly in identity verification and onboarding processes. Conventional detection methods often struggle to generalize against sophisticated facial manipulations. This study...

CVE-2025-54412

skops is a Python library which helps users share and ship their scikit-learn based models. Versions 0.11.0 and below contain a inconsistency in the OperatorFuncNode which can be exploited to hide the execution of untrusted operator methods. This can then be used in a code reuse attack to invoke...

CVE-2025-54412 skops' Inconsistent Trusted Type Validation Enables Hidden `operator` Methods Execution

skops is a Python library which helps users share and ship their scikit-learn based models. Versions 0.11.0 and below contain a inconsistency in the OperatorFuncNode which can be exploited to hide the execution of untrusted operator methods. This can then be used in a code reuse attack to invoke...

ModShift: Model Privacy Via Designed Shifts

In this paper, shifts are introduced to preserve model privacy against an eavesdropper in federated learning. Model learning is treated as a parameter estimation problem. This perspective allows us to derive the Fisher Information matrix of the model updates from the shifted updates and drive the...

Trivial Trojans: How Minimal MCP Servers Enable Cross-Tool Exfiltration of Sensitive Data

The Model Context Protocol MCP represents a significant advancement in AI-tool integration, enabling seamless communication between AI agents and external services. However, this connectivity introduces novel attack vectors that remain largely unexplored. This paper demonstrates how unsophisticat...

Information Exposure

Overview Affected versions of this package are vulnerable to Information Exposure via the q URL parameter in the /api/v2.0/users endpoint. An attacker can retrieve sensitive password hash and salt values by abusing the filtering capability to extract this information character by character. Note:...

Subliminal Learning in AIs

Today's freaky LLM behavior: We study subliminal learning, a surprising phenomenon where language models learn traits from model-generated data that is semantically unrelated to those traits. For example, a "student" model learns to prefer owls when trained on sequences of numbers generated by a...