Practical Adversarial Attacks on Stochastic Bandits Via Fake Data Injection

Adversarial attacks on stochastic bandits have traditionally relied on some unrealistic assumptions, such as per-round reward manipulation and unbounded perturbations, limiting their relevance to real-world systems. We propose a more practical threat model, Fake Data Injection, which reflects...

Security Concerns for Large Language Models: a Survey

Large Language Models LLMs such as GPT-4 and its recent iterations, Google's Gemini, Anthropic's Claude 3 models, and xAI's Grok have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. In this survey, we provide a comprehensive...

Safety Alignment Can Be Not Superficial with Explicit Safety Signals

Recent studies on the safety alignment of large language models LLMs have revealed that existing approaches often operate superficially, leaving models vulnerable to various adversarial attacks. Despite their significance, these studies generally fail to offer actionable solutions beyond data...

Preventing Adversarial AI Attacks against Autonomous Situational Awareness: a Maritime Case Study

Adversarial artificial intelligence AI attacks pose a significant threat to autonomous transportation, such as maritime vessels, that rely on AI components. Malicious actors can exploit these systems to deceive and manipulate AI-driven operations. This paper addresses three critical research...

Scalable Defense against In-The-Wild Jailbreaking Attacks with Safety Context Retrieval

Large Language Models LLMs are known to be vulnerable to jailbreaking attacks, wherein adversaries exploit carefully engineered prompts to induce harmful or unethical responses. Such threats have raised critical concerns about the safety and reliability of LLMs in real-world deployment. While...

GenAI Security: Outsmarting the Bots with a Proactive Testing Framework

The increasing sophistication and integration of Generative AI GenAI models into diverse applications introduce new security challenges that traditional methods struggle to address. This research explores the critical need for proactive security measures to mitigate the risks associated with...

Quantum Support Vector Regression for Robust Anomaly Detection

Anomaly Detection AD is critical in data analysis, particularly within the domain of IT security. In recent years, Machine Learning ML algorithms have emerged as a powerful tool for AD in large-scale data. In this study, we explore the potential of quantum ML approaches, specifically quantum kern...

Deploying AI Agents? Learn to Secure Them Before Hackers Strike Your Business

AI agents are changing the way businesses work. They can answer questions, automate tasks, and create better user experiences. But with this power comes new risks — like data leaks, identity theft, and malicious misuse. If your company is exploring or already using AI agents, you need to ask: Are...

A Comprehensive Analysis of Adversarial Attacks against Spam Filters

Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significant challenge to the effectiveness of these filters. This study investigates t...

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability

While machine learning has significantly advanced Network Intrusion Detection Systems NIDS, particularly within IoT environments where devices generate large volumes of data and are increasingly susceptible to cyber threats, these models remain vulnerable to adversarial attacks. Our research...

Analysis of the Vulnerability of Machine Learning Regression Models to Adversarial Attacks Using Data from 5G Wireless Networks

This article describes the process of creating a script and conducting an analytical study of a dataset using the DeepMIMO emulator. An advertorial attack was carried out using the FGSM method to maximize the gradient. A comparison is made of the effectiveness of binary classifiers in the task of...

Mitigating the Structural Bias in Graph Adversarial Defenses

In recent years, graph neural networks GNNs have shown great potential in addressing various graph structure-related downstream tasks. However, recent studies have found that current GNNs are susceptible to malicious adversarial attacks. Given the inevitable presence of adversarial attacks in the...

The Dark Side of Digital Twins: Adversarial Attacks on AI-Driven Water Forecasting

Digital twins DTs are improving water distribution systems by using real-time data, analytics, and prediction models to optimize operations. This paper presents a DT platform designed for a Spanish water supply network, utilizing Long Short-Term Memory LSTM networks to predict water consumption...

Adversarial Attacks on LLM-As-A-Judge Systems: Insights from Prompt Injections

LLM as judge systems used to assess text quality code correctness and argument strength are vulnerable to prompt injection attacks. We introduce a framework that separates content author attacks from system prompt attacks and evaluate five models Gemma 3.27B Gemma 3.4B Llama 3.2 3B GPT 4 and Clau...

Property-Preserving Hashing for $\Ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks

Perceptual hashing is used to detect whether an input image is similar to a reference image with a variety of security applications. Recently, they have been shown to succumb to adversarial input attacks which make small imperceptible changes to the input image yet the hashing algorithm does not...

Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification

Deep learning DL-based image classification models are essential for autonomous vehicle AV perception modules since incorrect categorization might have severe repercussions. Adversarial attacks are widely studied cyberattacks that can lead DL models to predict inaccurate output, such as incorrect...

Investigating Cybersecurity Incidents Using Large Language Models in Latest-Generation Wireless Networks

The purpose of research: Detection of cybersecurity incidents and analysis of decision support and assessment of the effectiveness of measures to counter information security threats based on modern generative models. The methods of research: Emulation of signal propagation data in MIMO systems,...

adversarial-attacks-white-black-box (=0.1.7) potentially affected by CVE-2025-25302 via rembg (=2.0.57)

rembg PYPI version =2.0.57 is affected by a known vulnerability. The following packages have a transitive dependency on rembg and may be impacted: - adversarial-attacks-white-black-box =0.1.7 Source cves: CVE-2025-25302 Source advisory: OSV:GHSA-59QH-FMM7-3G9Q...

adversarial-attacks-white-black-box (=0.1.7) potentially affected by CVE-2025-25301 via rembg (=2.0.57)

rembg PYPI version =2.0.57 is affected by a known vulnerability. The following packages have a transitive dependency on rembg and may be impacted: - adversarial-attacks-white-black-box =0.1.7 Source cves: CVE-2025-25301 Source advisory: OSV:GHSA-R5GX-C49X-H878...

adversarial-attacks-white-black-box (=0.1.7) potentially affected by CVE-2025-25301 via rembg (=2.0.57)

rembg PYPI version =2.0.57 is affected by a known vulnerability. The following packages have a transitive dependency on rembg and may be impacted: - adversarial-attacks-white-black-box =0.1.7 Source cves: CVE-2025-25301 Source advisory: OSV:PYSEC-2025-24...