Co-Evolutionary Defence of Active Directory Attack Graphs Via GNN-Approximated Dynamic Programming

Modern enterprise networks increasingly rely on Active Directory AD for identity and access management. However, this centralization exposes a single point of failure, allowing adversaries to compromise high-value assets. Existing AD defense approaches often assume static attacker behavior, but...

Diverging Towards Hallucination: Detection of Failures in Vision-Language Models Via Multi-Token Aggregation

Vision-language models VLMs now rival human performance on many multimodal tasks, yet they still hallucinate objects or generate unsafe text. Current hallucination detectors, e.g., single-token linear probing SLP and PTrue, typically analyze only the logit of the first generated token or just its...

Blockchain-Enabled Decentralized Privacy-Preserving Group Purchasing for Energy Plans

Retail energy markets are increasingly consumer-oriented, thanks to a growing number of energy plans offered by a plethora of energy suppliers, retailers and intermediaries. To maximize the benefits of competitive retail energy markets, group purchasing is an emerging paradigm that aggregates...

MPMA: Preference Manipulation Attack against Model Context Protocol

Model Context Protocol MCP standardizes interface mapping for large language models LLMs to access external data and tools, which revolutionizes the paradigm of tool selection and facilitates the rapid expansion of the LLM agent tool ecosystem. However, as the MCP is increasingly adopted,...

LARGO: Latent Adversarial Reflection through Gradient Optimization for Jailbreaking LLMs

Efficient red-teaming method to uncover vulnerabilities in Large Language Models LLMs is crucial. While recent attacks often use LLMs as optimizers, the discrete language space make gradient-based methods struggle. We introduce LARGO Latent Adversarial Reflection through Gradient Optimization, a...

AutoRAN: Weak-To-Strong Jailbreaking of Large Reasoning Models

This paper presents AutoRAN, the first automated, weak-to-strong jailbreak attack framework targeting large reasoning models LRMs. At its core, AutoRAN leverages a weak, less-aligned reasoning model to simulate the target model's high-level reasoning structures, generates narrative prompts, and...

Anti-Sensing: Defense against Unauthorized Radar-Based Human Vital Sign Sensing with Physically Realizable Wearable Oscillators

Recent advancements in Ultra-Wideband UWB radar technology have enabled contactless, non-line-of-sight vital sign monitoring, making it a valuable tool for healthcare. However, UWB radar's ability to capture sensitive physiological data, even through walls, raises significant privacy concerns,...

On the Security Risks of ML-Based Malware Detection Systems: a Survey

Malware presents a persistent threat to user privacy and data integrity. To combat this, machine learning-based ML-based malware detection MD systems have been developed. However, these systems have increasingly been attacked in recent years, undermining their effectiveness in practice. While the...

Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems Using Explainable AI

Federated Learning FL has emerged as a powerful paradigm for collaborative model training while keeping client data decentralized and private. However, it is vulnerable to Data Reconstruction Attacks DRA such as "LoKI" and "Robbing the Fed", where malicious models sent from the server to the clie...

GoLeash: Mitigating Golang Software Supply Chain Attacks with Runtime Policy Enforcement

Modern software supply chain attacks consist of introducing new, malicious capabilities into trusted third-party software components, in order to propagate to a victim through a package dependency chain. These attacks are especially concerning for the Go language ecosystem, which is extensively...

"Explain, Don'T Just Warn!" -- a Real-Time Framework for Generating Phishing Warnings with Contextual Cues

Anti-phishing tools typically display generic warnings that offer users limited explanation on why a website is considered malicious, which can prevent end-users from developing the mental models needed to recognize phishing cues on their own. This becomes especially problematic when these tools...

SynFuzz: Leveraging Fuzzing of Netlist to Detect Synthesis Bugs

In the evolving landscape of integrated circuit IC design, the increasing complexity of modern processors and intellectual property IP cores has introduced new challenges in ensuring design correctness and security. The recent advancements in hardware fuzzing techniques have shown their efficacy ...

WASP: Benchmarking Web Agent Security against Prompt Injection Attacks

Autonomous UI agents powered by AI have tremendous potential to boost human productivity by automating routine tasks such as filing taxes and paying bills. However, a major challenge in unlocking their full potential is security, which is exacerbated by the agent's ability to take action on their...

Decentralized Multi-Authority Attribute-Based Inner-Product Functional Encryption: Noisy and Evasive Constructions from Lattices

We study multi-authority attribute-based functional encryption for noisy inner-product functionality, and propose two new primitives: 1 multi-authority attribute-based noisy inner-product functional encryption MA-ABNIPFE, which generalizes existing multi-authority attribute-based IPFE schemes by...

Ivanti EPMM Pre-Auth RCE Chain 1day Detection Artifact Generator Tool

This script attempts to detect if Ivanti EPMM is vulnerable to CVE-2025-4427 and CVE-2025-4428. It affects versions 11.12.0.4 and prior, 12.3.0.1 and prior, 12.4.0.1 and prior, and 12.5.0.0 and prior...

From Trade-Off to Synergy: a Versatile Symbiotic Watermarking Framework for Large Language Models

The rise of Large Language Models LLMs has heightened concerns about the misuse of AI-generated text, making watermarking a promising solution. Mainstream watermarking schemes for LLMs fall into two categories: logits-based and sampling-based. However, current schemes entail trade-offs among...

PIG: Privacy Jailbreak Attack on LLMs Via Gradient-Based Iterative In-Context Optimization

Large Language Models LLMs excel in various domains but pose inherent privacy risks. Existing methods to evaluate privacy leakage in LLMs often use memorized prefixes or simple instructions to extract data, both of which well-alignment models can easily block. Meanwhile, Jailbreak attacks bypass...

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning

Federated Unlearning FU has emerged as a critical compliance mechanism for data privacy regulations, requiring unlearned clients to provide verifiable Proof of Federated Unlearning PoFU to auditors upon data removal requests. However, we uncover a significant privacy vulnerability: when gradient...

Side Channel Analysis in Homomorphic Encryption

Homomorphic encryption provides many opportunities for privacy-aware processing, including with methods related to machine learning. Many of our existing cryptographic methods have been shown in the past to be susceptible to side channel attacks. With these, the implementation of the cryptographi...

Mobius Forensic Toolkit 2.15

Mobius Forensic Toolkit is a forensic framework written in Python/GTK that manages cases and case items, providing an abstract interface for developing extensions. Cases and item categories are defined using XML files for easy integration with other tools...

The Ripple Effect: on Unforeseen Complications of Backdoor Attacks

Recent research highlights concerns about the trustworthiness of third-party Pre-Trained Language Models PTLMs due to potential backdoor attacks. These backdoored PTLMs, however, are effective only for specific pre-defined downstream tasks. In reality, these PTLMs can be adapted to many other...

ProxyPrompt: Securing System Prompts against Prompt Extraction Attacks

The integration of large language models LLMs into a wide range of applications has highlighted the critical role of well-crafted system prompts, which require extensive testing and domain expertise. These prompts enhance task performance but may also encode sensitive information and filtering...

Enhanced Multiuser CSI-Based Physical Layer Authentication Based on Information Reconciliation

This paper presents a physical layer authentication PLA technique using information reconciliation in multiuser communication systems. A cost-effective solution for low-end Internet of Things networks can be provided by PLA. In this work, we develop an information reconciliation scheme using Pola...

GenoArmory: a Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models

We propose the first unified adversarial attack benchmark for Genomic Foundation Models GFMs, named GenoArmory. Unlike existing GFM benchmarks, GenoArmory offers the first comprehensive evaluation framework to systematically assess the vulnerability of GFMs to adversarial attacks. Methodologicall...

Yet Another Diminishing Spark: Low-Level Cyberattacks in the Israel-Gaza Conflict

We report empirical evidence of web defacement and DDoS attacks carried out by low-level cybercrime actors in the Israel-Gaza conflict. Our quantitative measurements indicate an immediate increase in such cyberattacks following the Hamas-led assault and the subsequent declaration of war. However,...

Server-Side Template Injection Vulnerabilities and Exploitation Techniques

Research article called Server-Side Template Injection SSTI Vulnerabilities and Exploitation Techniques. The paper provides a structured methodology for detecting and exploiting SSTI vulnerabilities across multiple template engines, along with real-world case studies and mitigation strategies...

DMind Benchmark: toward a Holistic Assessment of LLM Capabilities across the Web3 Domain

Large Language Models LLMs have achieved impressive performance in diverse natural language processing tasks, but specialized domains such as Web3 present new challenges and require more tailored evaluation. Despite the significant user base and capital flows in Web3, encompassing smart contracts...

GuardReasoner-VL: Safeguarding VLMs Via Reinforced Reasoning

To enhance the safety of VLMs, this paper introduces a novel reasoning-based VLM guard model dubbed GuardReasoner-VL. The core idea is to incentivize the guard model to deliberatively reason before making moderation decisions via online RL. First, we construct GuardReasoner-VLTrain, a reasoning...

Optimal Allocation of Privacy Budget on Hierarchical Data Release

Releasing useful information from datasets with hierarchical structures while preserving individual privacy presents a significant challenge. Standard privacy-preserving mechanisms, and in particular Differential Privacy, often require careful allocation of a finite privacy budget across differen...

Privacy and Confidentiality Requirements Engineering for Process Data

The application and development of process mining techniques face significant challenges due to the lack of publicly available real-life event logs. One reason for companies to abstain from sharing their data are privacy and confidentiality concerns. Privacy concerns refer to personal data as...

Random Client Selection on Contrastive Federated Learning for Tabular Data

Vertical Federated Learning VFL has revolutionised collaborative machine learning by enabling privacy-preserving model training across multiple parties. However, it remains vulnerable to information leakage during intermediate computation sharing. While Contrastive Federated Learning CFL was...

On Technique Identification and Threat-Actor Attribution Using LLMs and Embedding Models

Attribution of cyber-attacks remains a complex but critical challenge for cyber defenders. Currently, manual extraction of behavioral indicators from dense forensic documentation causes significant attribution delays, especially following major incidents at the international scale. This research...

Managerial Insights on Investment Strategy in Cybersecurity: Findings from Multi-Country Research

This study examines the strategic role of cybersecurity based on survey data from 1,083 managers across Europe, the UK, and the United States. The findings indicate growing recognition of cybersecurity as a source of competitive advantage, although firms continue to face barriers such as limited...

AC-LoRA: (Almost) Training-Free Access Control-Aware Multi-Modal LLMs

Corporate LLMs are gaining traction for efficient knowledge dissemination and management within organizations. However, as current LLMs are vulnerable to leaking sensitive information, it has proven difficult to apply them in settings where strict access control is necessary. To this end, we desi...

Quantized Approximate Signal Processing (QASP): Towards Homomorphic Encryption for Audio

Audio and speech data are increasingly used in machine learning applications such as speech recognition, speaker identification, and mental health monitoring. However, the passive collection of this data by audio listening devices raises significant privacy concerns. Fully homomorphic encryption...

S3C2 Summit 2024-09: Industry Secure Software Supply Chain Summit

While providing economic and software development value, software supply chains are only as strong as their weakest link. Over the past several years, there has been an exponential increase in cyberattacks, specifically targeting vulnerable links in critical software supply chains. These attacks...

Enhancing IoT Cyber Attack Detection in the Presence of Highly Imbalanced Data

Due to the rapid growth in the number of Internet of Things IoT networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. A high rate of missed threats can be the result, as traditional machine learnin...

Agent Name Service (ANS): a Universal Directory for Secure AI Agent Discovery and Interoperability

The proliferation of AI agents requires robust mechanisms for secure discovery. This paper introduces the Agent Name Service ANS, a novel architecture based on DNS addressing the lack of a public agent discovery framework. ANS provides a protocol-agnostic registry infrastructure that leverages...

SafeTrans: LLM-Assisted Transpilation from C to Rust

Rust is a strong contender for a memory-safe alternative to C as a "systems" programming language, but porting the vast amount of existing C code to Rust is a daunting task. In this paper, we evaluate the potential of large language models LLMs to automate the transpilation of C code to idiomatic...

Neural-Inspired Advances in Integral Cryptanalysis

The study by Gohr et.al at CRYPTO 2019 and sunsequent related works have shown that neural networks can uncover previously unused features, offering novel insights into cryptanalysis. Motivated by these findings, we employ neural networks to learn features specifically related to integral...

Enhancing Secrecy Energy Efficiency in RIS-Aided Aerial Mobile Edge Computing Networks: a Deep Reinforcement Learning Approach

This paper studies the problem of securing task offloading transmissions from ground users against ground eavesdropping threats. Our study introduces a reconfigurable intelligent surface RIS-aided unmanned aerial vehicle UAV-mobile edge computing MEC scheme to enhance the secure task offloading...

Dark LLMs: the Growing Threat of Unaligned AI Models

Large Language Models LLMs rapidly reshape modern life, advancing fields from healthcare to education and beyond. However, alongside their remarkable capabilities lies a significant threat: the susceptibility of these models to jailbreaking. The fundamental vulnerability of LLMs to jailbreak...

The Tangent Space Attack

We propose a new method for retrieving the algebraic structure of a generic alternant code given an arbitrary generator matrix, provided certain conditions are met. We then discuss how this challenges the security of the McEliece cryptosystem instantiated with this family of codes. The central...

One for All: Formally Verifying Protocols Which Use Aggregate Signatures (Extended Version)

Aggregate signatures are digital signatures that compress multiple signatures from different parties into a single signature, thereby reducing storage and bandwidth requirements. BLS aggregate signatures are a popular kind of aggregate signature, deployed by Ethereum, Dfinity, and Cloudflare...

Cutting through Privacy: a Hyperplane-Based Data Reconstruction Attack in Federated Learning

Federated Learning FL enables collaborative training of machine learning models across distributed clients without sharing raw data, ostensibly preserving data privacy. Nevertheless, recent studies have revealed critical vulnerabilities in FL, showing that a malicious central server can manipulat...

AttentionGuard: Transformer-Based Misbehavior Detection for Secure Vehicular Platoons

Vehicle platooning, with vehicles traveling in close formation coordinated through Vehicle-to-Everything V2X communications, offers significant benefits in fuel efficiency and road utilization. However, it is vulnerable to sophisticated falsification attacks by authenticated insiders that can...

Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning

Federated learning FL enhances privacy and reduces communication cost for resource-constrained edge clients by supporting distributed model training at the edge. However, the heterogeneous nature of such devices produces diverse, non-independent, and identically distributed non-IID data, making t...

The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems Powered by Deep Learning

We study the security of stock price forecasting using Deep Learning DL in computational finance. Despite abundant prior research on the vulnerability of DL to adversarial perturbations, such work has hitherto hardly addressed practical adversarial threat models in the context of DL-powered...

SecReEvalBench: a Multi-Turned Security Resilience Evaluation Benchmark for Large Language Models

The increasing deployment of large language models in security-sensitive domains necessitates rigorous evaluation of their resilience against adversarial prompt-based attacks. While previous benchmarks have focused on security evaluations with limited and predefined attack domains, such as...

Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy

Large Language Models LLMs have gained significant popularity due to their remarkable capabilities in text understanding and generation. However, despite their widespread deployment in inference services such as ChatGPT, concerns about the potential leakage of sensitive user data have arisen...