MulVuln: Enhancing Pre-Trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detection

Software vulnerabilities SVs pose a critical threat to safety-critical systems, driving the adoption of AI-based approaches such as machine learning and deep learning for software vulnerability detection. Despite promising results, most existing methods are limited to a single programming languag...

Agentic Misalignment: How LLMs Could Be Insider Threats

We stress-tested 16 leading models from multiple developers in hypothetical corporate environments to identify potentially risky agentic behaviors before they cause real harm. In the scenarios, we allowed models to autonomously send emails and access sensitive information. They were assigned only...

Security Analysis of Ponzi Schemes in Ethereum Smart Contracts

The rapid advancement of blockchain technology has precipitated the widespread adoption of Ethereum and smart contracts across a variety of sectors. However, this has also given rise to numerous fraudulent activities, with many speculators embedding Ponzi schemes within smart contracts, resulting...

Pilot Contamination Attacks Detection with Machine Learning for Multi-User Massive MIMO

Massive multiple-input multiple-output MMIMO is essential to modern wireless communication systems, like 5G and 6G, but it is vulnerable to active eavesdropping attacks. One type of such attack is the pilot contamination attack PCA, where a malicious user copies pilot signals from an authentic us...

A Quantum-Secure Voting Framework Using QKD, Dual-Key Symmetric Encryption, and Verifiable Receipts

Electronic voting systems face growing risks from cyberattacks and data breaches, which are expected to intensify with the advent of quantum computing. To address these challenges, we introduce a quantum-secure voting framework that integrates Quantum Key Distribution QKD, Dual-Key Symmetric...

CST-AFNet: A Dual Attention-Based Deep Learning Framework for Intrusion Detection in IoT Networks

The rapid expansion of the Internet of Things IoT has revolutionized modern industries by enabling smart automation and real time connectivity. However, this evolution has also introduced complex cybersecurity challenges due to the heterogeneous, resource constrained, and distributed nature of...

PentestMCP: A Toolkit for Agentic Penetration Testing

Agentic AI is transforming security by automating many tasks being performed manually. While initial agentic approaches employed a monolithic architecture, the Model-Context-Protocol has now enabled a remote-procedure call RPC paradigm to agentic applications, allowing for the flexible constructi...

A Novel Unified Lightweight Temporal-Spatial Transformer Approach for Intrusion Detection in Drone Networks

The growing integration of drones across commercial, industrial, and civilian domains has introduced significant cybersecurity challenges, particularly due to the susceptibility of drone networks to a wide range of cyberattacks. Existing intrusion detection mechanisms often lack the adaptability,...

Amcache Evilhunter Tool

AmCache-EvilHunter is a command-line tool to parse and analyze Windows Amcache.hve registry hives, identify evidence of execution, suspicious executables, and integrate VirusTotal/OpenTIP lookups for enhanced threat intelligence...

A Statistical Method for Attack-Agnostic Adversarial Attack Detection with Compressive Sensing Comparison

Adversarial attacks present a significant threat to modern machine learning systems. Yet, existing detection methods often lack the ability to detect unseen attacks or detect different attack types with a high level of accuracy. In this work, we propose a statistical approach that establishes a...

CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption

Privacy-preserving machine learning has become an important long-term pursuit in this era of artificial intelligence AI. Fully Homomorphic Encryption FHE is a uniquely promising solution, offering provable privacy and security guarantees. Unfortunately, computational cost is impeding its mass...

A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT

The rapid growth of the Internet of Things IoT has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability in resource-constrained IoT environments. To address these...

Unmasking Puppeteers: Leveraging Biometric Leakage to Disarm Impersonation in AI-Based Videoconferencing

AI-based talking-head videoconferencing systems reduce bandwidth by sending a compact pose-expression latent and re-synthesizing RGB at the receiver, but this latent can be puppeteered, letting an attacker hijack a victim's likeness in real time. Because every frame is synthetic, deepfake and...

Explainable but Vulnerable: Adversarial Attacks on XAI Explanation in Cybersecurity Applications

Explainable Artificial Intelligence XAI has aided machine learning ML researchers with the power of scrutinizing the decisions of the black-box models. XAI methods enable looking deep inside the models' behavior, eventually generating explanations along with a perceived trust and transparency...

LegalSim: Multi-Agent Simulation of Legal Systems for Discovering Procedural Exploits

We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses in codified rules. Plaintiff and defendant agents choose from a constrained action space for example, discovery requests, motions, meet-and-confer,...

NEXUS: Network Exploration for EXploiting Unsafe Sequences in Multi-Turn LLM Jailbreaks

Large Language Models LLMs have revolutionized natural language processing but remain vulnerable to jailbreak attacks, especially multi-turn jailbreaks that distribute malicious intent across benign exchanges and bypass alignment mechanisms. Existing approaches often explore the adversarial space...

External Data Extraction Attacks against Retrieval-Augmented Large Language Models

In recent years, RAG has emerged as a key paradigm for enhancing large language models LLMs. By integrating externally retrieved information, RAG alleviates issues like outdated knowledge and, crucially, insufficient domain expertise. While effective, RAG introduces new risks of external data...

Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks

As deep learning models become widely deployed as components within larger production systems, their individual shortcomings can create system-level vulnerabilities with real-world impact. This paper studies how adversarial attacks targeting an ML component can degrade or bypass an entire...

A Cybersecurity AI Agent Selection and Decision Support Framework

This paper presents a novel, structured decision support framework that systematically aligns diverse artificial intelligence AI agent architectures, reactive, cognitive, hybrid, and learning, with the comprehensive National Institute of Standards and Technology NIST Cybersecurity Framework CSF...

RedCodeAgent: Automatic Red-Teaming Agent against Diverse Code Agents

Code agents have gained widespread adoption due to their strong code generation capabilities and integration with code interpreters, enabling dynamic execution, debugging, and interactive programming capabilities. While these advancements have streamlined complex workflows, they have also...

Authentication Security of PRF GNSS Ranging

This work derives the authentication security of pseudorandom function PRF GNSS ranging under multiple GNSS spoofing models, including the Security Code Estimation and Replay SCER spoofer. When GNSS ranging codes derive from a PRF utilizing a secret known only to the broadcaster, the spoofer cann...

MALF: A Multi-Agent LLM Framework for Intelligent Fuzzing of Industrial Control Protocols

Industrial control systems ICS are vital to modern infrastructure but increasingly vulnerable to cybersecurity threats, particularly through weaknesses in their communication protocols. This paper presents MALF Multi-Agent LLM Fuzzing Framework, an advanced fuzzing solution that integrates large...

SoK: Measuring What Matters for Closed-Loop Security Agents

Cybersecurity is a relentless arms race, with AI driven offensive systems evolving faster than traditional defenses can adapt. Research and tooling remain fragmented across isolated defensive functions, creating blind spots that adversaries exploit. Autonomous agents capable of integrating, explo...

OpenSSL Toolkit 3.6.0

OpenSSL is a robust, fully featured Open Source toolkit implementing the Secure Sockets Layer and Transport Layer Security protocols with full-strength cryptography world-wide. This is the 3.6 release...

TLoRa: Implementing TLS over LoRa for Secure HTTP Communication in IoT

We present TLoRa, an end-to-end architecture for HTTPS communication over LoRa by integrating TCP tunneling and a complete TLS 1.3 handshake. It enables a seamless and secure communication channel between WiFi-enabled end devices and the Internet over LoRa using an End Hub EH and a Net Relay NR...

FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AI

Fuzz testing has become a cornerstone technique for identifying software bugs and security vulnerabilities, with broad adoption in both industry and open-source communities. Directly fuzzing a function requires fuzz drivers, which translate random fuzzer inputs into valid arguments for the target...

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

This paper presents CADL Cognitive-Adaptive Deception Layer, an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning Random Forest, XGBoost, Neural Networks combined with behavioral...

WireTap: Breaking Server SGX via DRAM Bus Interposition

Whitepaper that delves into Intel’s Software Guard eXtension SGX. A common misconception is that physical attacks on SGX require expensive laboratory equipment, thus putting them out of reach of hobbyist-level attackers. In this work, the authors challenge this belief, showing how simple memory b...

Computational Monogamy of Entanglement and Non-Interactive Quantum Key Distribution

Quantum key distribution QKD enables Alice and Bob to exchange a secret key over a public, untrusted quantum channel. Compared to classical key exchange, QKD achieves everlasting security: after the protocol execution the key is secure against adversaries that can do unbounded computations. On th...

Optimal Untelegraphable Encryption and Implications for Uncloneable Encryption

We investigate the notion of untelegraphable encryption UTE, a quantum encryption primitive that is a special case of uncloneable encryption UE, where the adversary's capabilities are restricted to producing purely classical information rather than arbitrary quantum states. We present an...

Backdoor Attacks against Speech Language Models

Large Language Models LLMs and their multimodal extensions are becoming increasingly popular. One common approach to enable multimodality is to cascade domain-specific encoders with an LLM, making the resulting model inherit vulnerabilities from all of its components. In this work, we present the...

WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents

Multiple prompt injection attacks have been proposed against web agents. At the same time, various methods have been developed to detect general prompt injection attacks, but none have been systematically evaluated for web agents. In this work, we bridge this gap by presenting the first...

Breaking the Code: Security Assessment of AI Code Agents through Systematic Jailbreaking Attacks

Code-capable large language model LLM agents are increasingly embedded into software engineering workflows where they can read, write, and execute code, raising the stakes of safety-bypass "jailbreak" attacks beyond text-only settings. Prior evaluations emphasize refusal or harmful-text detection...

USBCoercer: A TinyUSB Based WPAD Coercion Device

USBCoercer turns an ESP32 development board with native USB-OTG into an Ethernet-over-USB gadget capable of coercing proxy configuration via WPAD. It builds on the TinyUSB Network Control Model NCM example and adds a minimalist DHCP server that injects DHCP option 252 WPAD/PAC and, additionally,...

HVAC-EAR: Eavesdropping Human Speech Using HVAC Systems

Pressure sensors are widely integrated into modern Heating, Ventilation and Air Conditioning HVAC systems. As they are sensitive to acoustic pressure, they can be a source of eavesdropping. This paper introduces HVAC-EAR, which reconstructs intelligible speech from low-resolution, noisy pressure...

POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment

Large Language Models LLMs are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs offer cyber threat intelligence CTI to support vulnerability assessment and incident response. While recent work has shown that LLMs can support a wid...

Securing IoT Devices in Smart Cities: A Review of Proposed Solutions

Privacy and security in Smart Cities remain at constant risk due to the vulnerabilities introduced by Internet of Things IoT devices. The limited computational resources of these devices make them especially susceptible to attacks, while their widespread adoption increases the potential impact of...

American Fuzzy Lop plus plus 4.34c

Google's American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple but rock-solid instrumentation-guided genetic algorithm. afl++ is a superior fork to Google's afl. It has more speed, more and better mutations, more and better instrumentation, custom module support, etc...

Red Teaming Program Repair Agents: When Correct Patches Can Hide Vulnerabilities

LLM-based agents are increasingly deployed for software maintenance tasks such as automated program repair APR. APR agents automatically fetch GitHub issues and use backend LLMs to generate patches that fix the reported bugs. However, existing work primarily focuses on the functional correctness ...

SecureBERT 2.0: Advanced Language Model for Cybersecurity Intelligence

Effective analysis of cybersecurity and threat intelligence data demands language models that can interpret specialized terminology, complex document structures, and the interdependence of natural language and source code. Encoder-only transformer architectures provide efficient and robust...

LLM-Generated Samples for Android Malware Detection

Android malware continues to evolve through obfuscation and polymorphism, posing challenges for both signature-based defenses and machine learning models trained on limited and imbalanced datasets. Synthetic data has been proposed as a remedy for scarcity, yet the role of large language models LL...

Selmer-Inspired Elliptic Curve Generation

Elliptic curve cryptography ECC is foundational to modern secure communication, yet existing standard curves have faced scrutiny for opaque parameter-generation practices. This work introduces a Selmer-inspired framework for constructing elliptic curves that is both transparent and auditable...

OpenSSL Toolkit 3.0.18

OpenSSL is a robust, fully featured Open Source toolkit implementing the Secure Sockets Layer and Transport Layer Security protocols with full-strength cryptography world-wide. This is the 3.0 LTS release...

Cloud Investigation Automation Framework (CIAF): An AI-Driven Approach to Cloud Forensics

Large Language Models LLMs have gained prominence in domains including cloud security and forensics. Yet cloud forensic investigations still rely on manual analysis, making them time-consuming and error-prone. LLMs can mimic human reasoning, offering a pathway to automating cloud log analysis. To...

Dynamic Causal Attack Graph Based Cyber-Security Risk Assessment Framework for CTCS System

Protecting the security of the train control system is a critical issue to ensure the safe and reliable operation of high-speed trains. Scientific modeling and analysis for the security risk is a promising way to guarantee system security. However, the representation and assessment of the...

CHAI: Command Hijacking against Embodied AI

Embodied Artificial Intelligence AI promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training distributions and adapt to novel real-world situations. These capabilities, however, al...

MAVUL: Multi-Agent Vulnerability Detection Via Contextual Reasoning and Interactive Refinement

The widespread adoption of open-source software OSS necessitates the mitigation of vulnerability risks. Most vulnerability detection VD methods are limited by inadequate contextual understanding, restrictive single-round interactions, and coarse-grained evaluations, resulting in undesired model...

From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization

Large language models show promise for vulnerability discovery, yet prevailing methods inspect code in isolation, struggle with long contexts, and focus on coarse function- or file-level detections - offering limited actionable guidance to engineers who need precise line-level localization and...

Better Privilege Separation for Agents by Restricting Data Types

Large language models LLMs have become increasingly popular due to their ability to interact with unstructured content. As such, LLMs are now a key driver behind the automation of language processing systems, such as AI agents. Unfortunately, these advantages have come with a vulnerability to...

SoK: Systematic Analysis of Adversarial Threats against Deep Learning Approaches for Autonomous Anomaly Detection Systems in SDN-IoT Networks

Integrating SDN and the IoT enhances network control and flexibility. DL-based AAD systems improve security by enabling real-time threat detection in SDN-IoT networks. However, these systems remain vulnerable to adversarial attacks that manipulate input data or exploit model weaknesses,...