Data Poisoning Vulnerabilities across Healthcare AI Architectures: A Security Threat Analysis

Healthcare AI systems face major vulnerabilities to data poisoning that current defenses and regulations cannot adequately address. We analyzed eight attack scenarios in four categories: architectural attacks on convolutional neural networks, large language models, and reinforcement learning...

Adaptive Intrusion Detection for Evolving RPL IoT Attacks Using Incremental Learning

The routing protocol for low-power and lossy networks RPL has become the de facto routing standard for resource-constrained IoT systems, but its lightweight design exposes critical vulnerabilities to a wide range of routing-layer attacks such as hello flood, decreased rank, and version number...

HP Integrated Lights-Out Improper Neutralization of Input During Web Page Generation (CVE-2021-29206)

"A remote xss vulnerability was discovered in HPE Integrated Lights-Out 4 iLO 4 %NASLMINLEVEL 80900 C Tenable, Inc. include'compat.inc'; if description scriptid504401; scriptversion"1.1"; scriptsetattributeattribute:"pluginmodificationdate", value:"2025/11/13"; scriptcveid"CVE-2021-29206";...

MTAttack: Multi-Target Backdoor Attacks against Large Vision-Language Models

Recent advances in Large Visual Language Models LVLMs have demonstrated impressive performance across various vision-language tasks by leveraging large-scale image-text pretraining and instruction tuning. However, the security vulnerabilities of LVLMs have become increasingly concerning,...

How Worrying Are Privacy Attacks against Machine Learning?

In several jurisdictions, the regulatory framework on the release and sharing of personal data is being extended to machine learning ML. The implicit assumption is that disclosing a trained ML model entails a privacy risk for any personal data used in training comparable to directly releasing tho...

Can AI Models Be Jailbroken to Phish Elderly Victims? an End-To-End Evaluation

We present an end-to-end demonstration of how attackers can exploit AI safety failures to harm vulnerable populations: from jailbreaking LLMs to generate phishing content, to deploying those messages against real targets, to successfully compromising elderly victims. We systematically evaluated...

Phantom Menace: Exploring and Enhancing the Robustness of VLA Models against Physical Sensor Attacks

Vision-Language-Action VLA models revolutionize robotic systems by enabling end-to-end perception-to-action pipelines that integrate multiple sensory modalities, such as visual signals processed by cameras and auditory signals captured by microphones. This multi-modality integration allows VLA...

One Signature, Multiple Payments: Demystifying and Detecting Signature Replay Vulnerabilities in Smart Contracts

Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verification, smart contracts can ensure that signers possess the required permissions, thus enhancing security and scalability...

How Can We Effectively Use LLMs for Phishing Detection?: Evaluating the Effectiveness of Large Language Model-Based Phishing Detection Models

Large language models LLMs have emerged as a promising phishing detection mechanism, addressing the limitations of traditional deep learning-based detectors, including poor generalization to previously unseen websites and a lack of interpretability. However, LLMs' effectiveness for phishing...

Taught by the Flawed: How Dataset Insecurity Breeds Vulnerable AI Code

AI programming assistants have demonstrated a tendency to generate code containing basic security vulnerabilities. While developers are ultimately responsible for validating and reviewing such outputs, improving the inherent quality of these generated code snippets remains essential. A key...

StyleBreak: Revealing Alignment Vulnerabilities in Large Audio-Language Models Via Style-Aware Audio Jailbreak

Large Audio-language Models LAMs have recently enabled powerful speech-based interactions by coupling audio encoders with Large Language Models LLMs. However, the security of LAMs under adversarial attacks remains underexplored, especially through audio jailbreaks that craft malicious audio promp...

EUVD-2025-106750

A vulnerability has been identified in LOGO! 12/24RCE 6ED1052-1MD08-0BA2 All versions, LOGO! 12/24RCEo 6ED1052-2MD08-0BA2 All versions, LOGO! 230RCE 6ED1052-1FB08-0BA2 All versions, LOGO! 230RCEo 6ED1052-2FB08-0BA2 All versions, LOGO! 24CE 6ED1052-1CC08-0BA2 All versions, LOGO! 24CEo...

Cisco Finds Open-Weight AI Models Easy to Exploit in Long Chats

Cisco’s new research shows that open-weight AI models, while driving innovation, face serious security risks as multi-turn attacks, including conversational persistence, can bypass safeguards and expose data...

From LLMs to Agents: A Comparative Evaluation of LLMs and LLM-Based Agents in Security Patch Detection

The widespread adoption of open-source software OSS has accelerated software innovation but also increased security risks due to the rapid propagation of vulnerabilities and silent patch releases. In recent years, large language models LLMs and LLM-based agents have demonstrated remarkable...

Siemens LOGO! 访问控制错误漏洞

Siemens LOGO! is a programmable logic controller from Siemens Germany. An access control error vulnerability exists in Siemens LOGO! that arises from the absence of certain authentication, which could allow an unauthenticated, remote attacker to alter the device's time, which could in turn affect...

PT-2025-46543

Name of the Vulnerable Software and Affected Versions LOGO! 12/24RCE 6ED1052-1MD08-0BA2 affected versions not specified LOGO! 12/24RCEo 6ED1052-2MD08-0BA2 affected versions not specified LOGO! 230RCE 6ED1052-1FB08-0BA2 affected versions not specified LOGO! 230RCEo 6ED1052-2FB08-0BA2 affected...

Publish Your Threat Models! the Benefits Far Outweigh the Dangers

Threat modeling has long guided software development work, and we consider how Public Threat Models PTM can convey useful security information to others. We list some early adopter precedents, explain the many benefits, address potential objections, and cite regulatory drivers. Internal threat...

NVIDIA Megatron-LM 代码注入漏洞

NVIDIA Megatron-LM is a PyTorch-based distributed training framework from NVIDIA that is specifically designed for training large Transformer language models. NVIDIA Megatron-LM suffers from a code injection vulnerability that stems from scripts improperly handling malicious data, which could lea...

DrAttack

DrAttack: Prompt Decomposition and Reconstruction Makes Powerf...

JPRO: Automated Multimodal Jailbreaking Via Multi-Agent Collaboration Framework

The widespread application of large VLMs makes ensuring their secure deployment critical. While recent studies have demonstrated jailbreak attacks on VLMs, existing approaches are limited: they require either white-box access, restricting practicality, or rely on manually crafted patterns, leadin...