Active Sybil Attack and Efficient Defense Strategy in IPFS DHT

The InterPlanetary File System IPFS is a decentralized peer-to-peer P2P storage that relies on Kademlia, a Distributed Hash Table DHT structure commonly used in P2P systems for its proved scalability. However, DHTs are known to be vulnerable to Sybil attacks, in which a single entity controls...

Poster: Machine Learning for Vulnerability Detection As Target Oracle in Automated Fuzz Driver Generation

In vulnerability detection, machine learning has been used as an effective static analysis technique, although it suffers from a significant rate of false positives. Contextually, in vulnerability discovery, fuzzing has been used as an effective dynamic analysis technique, although it requires...

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures

As large language models LLMs continue to evolve, it is critical to assess the security threats and vulnerabilities that may arise both during their training phase and after models have been deployed. This survey seeks to define and categorize the various attacks targeting LLMs, distinguishing...

Allocation of Heterogeneous Resources in General Lotto Games

The allocation of resources plays an important role in the completion of system objectives and tasks, especially in the presence of strategic adversaries. Optimal allocation strategies are becoming increasingly more complex, given that multiple heterogeneous types of resources are at a system...

Watermark Overwriting Attack on StegaStamp Algorithm

This paper presents an attack method on the StegaStamp watermarking algorithm that completely removes watermarks from an image with minimal quality loss, developed as part of the NeurIPS "Erasing the invisible" competition...

SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)

This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B Detailed DHC-B as part of the 2020 Census. The tabulations contain household statistics about household type and tenure...

The DCR Delusion: Measuring the Privacy Risk of Synthetic Data

Synthetic data has become an increasingly popular way to share data without revealing sensitive information. Though Membership Inference Attacks MIAs are widely considered the gold standard for empirically assessing the privacy of a synthetic dataset, practitioners and researchers often rely on...

Explainable Machine Learning for Cyberattack Identification from Traffic Flows

The increasing automation of traffic management systems has made them prime targets for cyberattacks, disrupting urban mobility and public safety. Traditional network-layer defenses are often inaccessible to transportation agencies, necessitating a machine learning-based approach that relies sole...

A Rusty Link in the AI Supply Chain: Detecting Evil Configurations in Model Repositories

Recent advancements in large language models LLMs have spurred the development of diverse AI applications from code generation and video editing to text generation; however, AI supply chains such as Hugging Face, which host pretrained models and their associated configuration files contributed by...

Machine Learning for Cyber-Attack Identification from Traffic Flows

This paper presents our simulation of cyber-attacks and detection strategies on the traffic control system in Daytona Beach, FL. using Raspberry Pi virtual machines and the OPNSense firewall, along with traffic dynamics from SUMO and exploitation via the Metasploit framework. We try to answer the...

Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning

This paper presents a holistic approach to attacker preference modeling from system-level audit logs using inverse reinforcement learning IRL. Adversary modeling is an important capability in cybersecurity that lets defenders characterize behaviors of potential attackers, which enables attributio...

Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks

Hierarchical Federated Learning HFL has recently emerged as a promising solution for intelligent decision-making in vehicular networks, helping to address challenges such as limited communication resources, high vehicle mobility, and data heterogeneity. However, HFL remains vulnerable to...

Notes on Univariate Sumcheck

These notes describe an adaptation of the multivariate sumcheck protocol to univariate polynomials interpolated over roots of unity...

Attack and Defense Techniques in Large Language Models: a Survey and New Perspectives

Large Language Models LLMs have become central to numerous natural language processing tasks, but their vulnerabilities present significant security and ethical challenges. This systematic survey explores the evolving landscape of attack and defense techniques in LLMs. We classify attacks into...

Non-Adaptive Cryptanalytic Time-Space Lower Bounds Via a Shearer-Like Inequality for Permutations

Whitepaper called Non-Adaptive Cryptanalytic Time-Space Lower Bounds Via A Shearer-Like Inequality For Permutations...

Decentralized Vulnerability Disclosure Via Permissioned Blockchain: a Secure, Transparent Alternative to Centralized CVE Management

This paper proposes a decentralized, blockchain-based system for the publication of Common Vulnerabilities and Exposures CVEs, aiming to mitigate the limitations of the current centralized model primarily overseen by MITRE. The proposed architecture leverages a permissioned blockchain, wherein on...

Spill the Beans: Exploiting CPU Cache Side-Channels to Leak Tokens from Large Language Models

Side-channel attacks on shared hardware resources increasingly threaten confidentiality, especially with the rise of Large Language Models LLMs. In this work, we introduce Spill The Beans, a novel application of cache side-channels to leak tokens generated by an LLM. By co-locating an attack...

Auditing without Leaks Despite Curiosity

Whitepaper called Auditing Without Leaks Despite Curiosity...

RevealNet: Distributed Traffic Correlation for Attack Attribution on Programmable Networks

Network attackers have increasingly resorted to proxy chains, VPNs, and anonymity networks to conceal their activities. To tackle this issue, past research has explored the applicability of traffic correlation techniques to perform attack attribution, i.e., to identify an attacker's true network...

Confidential Serverless Computing

Although serverless computing offers compelling cost and deployment simplicity advantages, a significant challenge remains in securely managing sensitive data as it flows through the network of ephemeral function executions in serverless computing environments within untrusted clouds. While...

HoneyWin: High-Interaction Windows Honeypot in Enterprise Environment

Windows operating systems OS are ubiquitous in enterprise Information Technology IT and operational technology OT environments. Due to their widespread adoption and known vulnerabilities, they are often the primary targets of malware and ransomware attacks. With 93% of the ransomware targeting...

Can Differentially Private Fine-Tuning LLMs Protect against Privacy Attacks?

Fine-tuning large language models LLMs has become an essential strategy for adapting them to specialized tasks; however, this process introduces significant privacy challenges, as sensitive training data may be inadvertently memorized and exposed. Although differential privacy DP offers strong...

Addressing Noise and Stochasticity in Fraud Detection for Service Networks

Fraud detection is crucial in social service networks to maintain user trust and improve service network security. Existing spectral graph-based methods address this challenge by leveraging different graph filters to capture signals with different frequencies in service networks. However, most...

Enhancing the Cloud Security through Topic Modelling

Protecting cloud applications is crucial in an age where security constantly threatens the digital world. The inevitable cyber-attacks throughout the CI/CD pipeline make cloud security innovations necessary. This research is motivated by applying Natural Language Processing NLP methodologies, suc...

Protocol-Agnostic and Data-Free Backdoor Attacks on Pre-Trained Models in RF Fingerprinting

While supervised deep neural networks DNNs have proven effective for device authentication via radio frequency RF fingerprinting, they are hindered by domain shift issues and the scarcity of labeled data. The success of large language models has led to increased interest in unsupervised pre-train...

Packet Storm New Exploits for April, 2025

This archive contains all of the 166 exploits added to Packet Storm in April, 2025...

Zero-Day Botnet Attack Detection in IoV: a Modular Approach Using Isolation Forests and Particle Swarm Optimization

The Internet of Vehicles IoV is transforming transportation by enhancing connectivity and enabling autonomous driving. However, this increased interconnectivity introduces new security vulnerabilities. Bot malware and cyberattacks pose significant risks to Connected and Autonomous Vehicles CAVs, ...

Ai.Txt: a Domain-Specific Language for Guiding AI Interactions with the Internet

We introduce ai.txt, a novel domain-specific language DSL designed to explicitly regulate interactions between AI models, agents, and web content, addressing critical limitations of the widely adopted robots.txt standard. As AI increasingly engages with online materials for tasks such as training...

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...

AI-Driven IRM: Transforming Insider Risk Management with Adaptive Scoring and LLM-Based Threat Detection

Insider threats pose a significant challenge to organizational security, often evading traditional rule-based detection systems due to their subtlety and contextual nature. This paper presents an AI-powered Insider Risk Management IRM system that integrates behavioral analytics, dynamic risk...

Preserving Privacy and Utility in LLM-Based Product Recommendations

Large Language Model LLM-based recommendation systems leverage powerful language models to generate personalized suggestions by processing user interactions and preferences. Unlike traditional recommendation systems that rely on structured data and collaborative filtering, LLM-based models proces...

WordPress WP-Advanced-Search 3.3.9.3 Shell Upload

WordPress WP-Advanced-Search plugin versions 3.3.9.3 and below suffer from a remote shell upload vulnerability...

From Texts to Shields: Convergence of Large Language Models and Cybersecurity

This report explores the convergence of large language models LLMs and cybersecurity, synthesizing interdisciplinary insights from network security, artificial intelligence, formal methods, and human-centered design. It examines emerging applications of LLMs in software and network security, 5G...

OET: Optimization-Based Prompt Injection Evaluation Toolkit

Large Language Models LLMs have demonstrated remarkable capabilities in natural language understanding and generation, enabling their widespread adoption across various domains. However, their susceptibility to prompt injection attacks poses significant security risks, as adversarial inputs can...

Apple AirPlay Command Execution

Proof of concept exploit demonstrating the Apple AirPlay vulnerability as noted in CVE-2025-24271...

A Novel Feature-Aware Chaotic Image Encryption Scheme for Data Security and Privacy in IoT and Edge Networks

The security of image data in the Internet of Things IoT and edge networks is crucial due to the increasing deployment of intelligent systems for real-time decision-making. Traditional encryption algorithms such as AES and RSA are computationally expensive for resource-constrained IoT devices and...

PatchFuzz: Patch Fuzzing for JavaScript Engines

Patch fuzzing is a technique aimed at identifying vulnerabilities that arise from newly patched code. While researchers have made efforts to apply patch fuzzing to testing JavaScript engines with considerable success, these efforts have been limited to using ordinary test cases or publicly...

Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services

Due to the increasing number of tasks that are solved on remote servers, identifying and classifying traffic is an important task to reduce the load on the server. There are various methods for classifying traffic. This paper discusses machine learning models for solving this problem. However, su...

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding

Binary code analysis plays a pivotal role in the field of software security and is widely used in tasks such as software maintenance, malware detection, software vulnerability discovery, patch analysis, etc. However, unlike source code, reverse engineers face significant challenges in understandi...

From Ahead-of- to Just-in-Time and Back Again: Static Analysis for Unix Shell Programs

Shell programming is as prevalent as ever. It is also quite complex, due to the structure of shell programs, their use of opaque software components, and their complex interactions with the broader environment. As a result, even when exercising an abundance of care, shell developers discover...

Unlocking User-Oriented Pages: Intention-Driven Black-Box Scanner for Real-World Web Applications

Black-box scanners have played a significant role in detecting vulnerabilities for web applications. A key focus in current black-box scanning is increasing test coverage i.e., accessing more web pages. However, since many web applications are user-oriented, some deep pages can only be accessed...

LASHED: LLMs and Static Hardware Analysis for Early Detection of RTL Bugs

While static analysis is useful in detecting early-stage hardware security bugs, its efficacy is limited because it requires information to form checks and is often unable to explain the security impact of a detected vulnerability. Large Language Models can be useful in filling these gaps by...

A Comprehensive Study of Exploitable Patterns in Smart Contracts: from Vulnerability to Defense

With the rapid advancement of blockchain technology, smart contracts have enabled the implementation of increasingly complex functionalities. However, ensuring the security of smart contracts remains a persistent challenge across the stages of development, compilation, and execution...

The Planted Orthogonal Vectors Problem

In the $k$-Orthogonal Vectors $k$-OV problem we are given $k$ sets, each containing $n$ binary vectors of dimension $d=n^o1$, and our goal is to pick one vector from each set so that at each coordinate at least one vector has a zero. It is a central problem in fine-grained complexity, conjectured...

Generative AI in Financial Institution: a Global Survey of Opportunities, Threats, and Regulation

Generative Artificial Intelligence GenAI is rapidly reshaping the global financial landscape, offering unprecedented opportunities to enhance customer engagement, automate complex workflows, and extract actionable insights from vast financial data. This survey provides an overview of GenAI adopti...

VDDP: Verifiable Distributed Differential Privacy under the Client-Server-Verifier Setup

Despite differential privacy DP often being considered the de facto standard for data privacy, its realization is vulnerable to unfaithful execution of its mechanisms by servers, especially in distributed settings. Specifically, servers may sample noise from incorrect distributions or generate...

Hoist with His Own Petard: Inducing Guardrails to Facilitate Denial-Of-Service Attacks on Retrieval-Augmented Generation of LLMs

Whitepaper called Hoist With His Own Petard: Inducing Guardrails To Facilitate Denial-Of-Service Attacks On Retrieval-Augmented Generation Of LLMs...

Protocol Dialects As Formal Patterns: a Composable Theory of Lingos -- Technical Report

Protocol dialects are methods for modifying protocols that provide light-weight security, especially against easy attacks that can lead to more serious ones. A lingo is a dialect's key security component by making attackers unable to "speak" the lingo. A lingo's "talk" changes all the time,...

Active Light Modulation to Counter Manipulation of Speech Visual Content

High-profile speech videos are prime targets for falsification, owing to their accessibility and influence. This work proposes Spotlight, a low-overhead and unobtrusive system for protecting live speech videos from visual falsification of speaker identity and lip and facial motion. Unlike...

Low Latency FPGA Implementation of Twisted Edward Curve Cryptography Hardware Accelerator over Prime Field

The performance of any elliptic curve cryptography hardware accelerator significantly relies on the efficiency of the underlying point multiplication PM architecture. This article presents a hardware implementation of field-programmable gate array FPGA based modular arithmetic, group operation, a...