The Role of Learning in Attacking Intrusion Detection Systems

Recent work on network attacks have demonstrated that ML-based network intrusion detection systems NIDS can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. ...

Protecting Context and Prompts: Deterministic Security for Non-Deterministic AI

Large Language Model LLM applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot prevent. We introduce two novel primitives--authenticated prompts and authenticated context--that provide cryptographically verifiable provenance acros...

Can Developers Rely on LLMs for Secure IaC Development?

We investigated the capabilities of GPT-4o and Gemini 2.0 Flash for secure Infrastructure as Code IaC development. For security smell detection, on the Stack Overflow dataset, which primarily contains small, simplified code snippets, the models detected at least 71% of security smells when prompt...

Reference-Free EM Validation Flow for Detecting Triggered Hardware Trojans

Hardware Trojans HTs threaten the trust and reliability of integrated circuits ICs, particularly when triggered HTs remain dormant during standard testing and activate only under rare conditions. Existing electromagnetic EM side-channel-based detection techniques often rely on golden references o...

WordPress Blubrry PowerPress 6.0 Cross Site Scripting

A cross site scripting vulnerability exists in Blubrry PowerPress WordPress Plugin version 6.0. The vulnerability allows remote attackers to inject arbitrary web script or HTML. This issue is older research added to the archive...

FreshRSS 1.11.1 Cross Site Scripting

Multiple cross site scripting vulnerabilities exist in FreshRSS version 1.11.1. The vulnerabilities allow remote attackers to inject arbitrary web script or HTML. This issue is older research added to the archive...

IlchCMS 2.1.37 Cross Site Scripting

A cross site scripting vulnerability exists in IlchCMS version 2.1.37. The vulnerability allows remote attackers to inject arbitrary web script or HTML. This issue is older research added to the archive...

miniBB 3.1 Cross Site Scripting

A cross site scripting vulnerability exists in miniBB Forum version 3.1. The vulnerability allows remote attackers to inject arbitrary web script or HTML. This issue is older research added to the archive...

Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents

Large language models LLMs have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to limited interaction, weak execution grounding, and a lack of experience reuse. We propose Co-RedTeam, a security-aware...

Stealthy Poisoning Attacks Bypass Defenses in Regression Settings

Regression models are widely used in industrial processes, engineering and in natural and physical sciences, yet their robustness to poisoning has received less attention. When it has, studies often assume unrealistic threat models and are thus less useful in practice. In this paper, we propose a...

Reference-Free Spectral Analysis of EM Side-Channels for Always-On Hardware Trojan Detection

Always-on hardware Trojans HTs pose a critical risk to trusted microelectronics, yet most side-channel detection methods rely on unavailable golden references. We present a reference-free approach that combines time-frequency EM analysis with Gaussian Mixture Models GMMs. By applying Short-Time...

Eclipse Attacks on Ethereum'S Peer-To-Peer Network

Eclipse attacks isolate blockchain nodes by monopolizing their peer-to-peer connections. The attacks were extensively studied in Bitcoin SP'15, SP'20, CCS'21, SP'23 and Monero NDSS'25, but their practicality against Ethereum nodes remains underexplored, particularly in the post-Merge settings. We...

DCeption: Real-World Wireless Man-In-The-Middle Attacks against CCS EV Charging

The adoption of Electric Vehicles EVs is happening at a rapid pace. To ensure fast and safe charging, complex communication is required between the vehicle and the charging station. In the globally used Combined Charging System CCS, this communication is carried over the HomePlug Green PHY HPGP...

AI Agents Vs. Human Investigators: Balancing Automation, Security, and Expertise in Cyber Forensic Analysis

In an era where cyber threats are rapidly evolving, the reliability of cyber forensic analysis has become increasingly critical for effective digital investigations and cybersecurity responses. AI agents are being adopted across digital forensic practices due to their ability to automate processe...

PrivFly: A Privacy-Preserving Self-Supervised Framework for Rare Attack Detection in IoFT

The Internet of Flying Things IoFT plays a vital role in modern applications such as aerial surveillance and smart mobility. However, it remains highly vulnerable to cyberattacks that threaten the confidentiality, integrity, and availability of sensitive data. Developing effective intrusion...

Many Hands Make Light Work: An LLM-Based Multi-Agent System for Detecting Malicious PyPI Packages

Malicious code in open-source repositories such as PyPI poses a growing threat to software supply chains. Traditional rule-based tools often overlook the semantic patterns in source code that are crucial for identifying adversarial components. Large language models LLMs show promise for software...

WMI Event Subscription Interval Persistence

This Metasploit module will create a permanent WMI event subscription to achieve file-less persistence using an event filter that triggers the payload after the specified CALLBACKINTERVAL. If the persistence is not installed, it will keep triggering payloads to spawn. Additionally a custom comman...

SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks

The rapid proliferation of Internet of Things IoT devices across domains such as smart homes, industrial control systems, and healthcare networks has significantly expanded the attack surface for cyber threats, including botnet-driven distributed denial-of-service DDoS, malware injection, and dat...

The Echo Chamber Multi-Turn LLM Jailbreak

The availability of Large Language Models LLMs has led to a new generation of powerful chatbots that can be developed at relatively low cost. As companies deploy these tools, security challenges need to be addressed to prevent financial loss and reputational damage. A key security challenge is...

Jailbreaking Large Language Models through Iterative Tool-Disguised Attacks Via Reinforcement Learning

Large language models LLMs have demonstrated remarkable capabilities across diverse applications, however, they remain critically vulnerable to jailbreak attacks that elicit harmful responses violating human values and safety guidelines. Despite extensive research on defense mechanisms, existing...

TP-Link TL-WR820N 2.80 Weak Cryptography

TP-Link TL-WR820N version 2.80 uses weak cryptographic algorithms for SSH...

tcpdump 4.99.6

tcpdump allows you to dump the traffic on a network. It can be used to print out the headers and/or contents of packets on a network interface that matches a given expression. You can use this tool to track down network problems, to detect many attacks, or to monitor the network activities...

Improving Router Security Using BERT

Previous work on home router security has shown that using system calls to train a transformer-based language model built on a BERT-style encoder using contrastive learning is effective in detecting several types of malware, but the performance remains limited at low false positive rates. In this...

PDPL Metric: Validating a Scale to Measure Personal Data Privacy Literacy among University Students

Personal data privacy literacy PDPL refers to a collection of digital literacy skills related to an individuals ability to understand, evaluate, and manage the collection, use, and protection of personal data in online and digital environments. This study introduces and validates a new psychometr...

Exploring the Integration of Differential Privacy in Cybersecurity Analytics: Balancing Data Utility and Privacy in Threat Intelligence

To resolve the acute problem of privacy protection and guarantee that data can be used in the context of threat intelligence, this paper considers the implementation of Differential Privacy DP in cybersecurity analytics. DP, which is a sound mathematical framework, ensures privacy by adding a...

Towards Eco Friendly Cybersecurity: Machine Learning Based Anomaly Detection with Carbon and Energy Metrics

The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network...

Quantum-Resistant Cryptographic Models for Next-Gen Cybersecurity

Another threat is the development of large quantum computers, which have a high likelihood of breaking the high popular security protocols because it can use both Shor and Grover algorithms. In order to fix this looming threat, quantum-resistant cryptographic systems, otherwise known as...

DeepGuard: Defending Deep Joint Source-Channel Coding against Eavesdropping at Physical-Layer

Deep joint source-channel coding DeepJSCC has emerged as a promising paradigm for efficient and robust information transmission. However, its intrinsic characteristics also pose new security challenges, notably an increased vulnerability to eavesdropping attacks. Existing studies on defending...

Large Language Models As a (Bad) Security Norm in the Context of Regulation and Compliance

The use of Large Language Models LLM by providers of cybersecurity and digital infrastructures of all kinds is an ongoing development. It is suggested and on an experimental basis used to write the code for the systems, and potentially fed with sensitive data or what would otherwise be considered...

CAPIO: Safe Kernel-Bypass of Commodity Devices Using Capabilities

Securing low-latency I/O in commodity systems forces a fundamental trade-off: rely on the kernel's high overhead mediated interface, or bypass it entirely, exposing sensitive hardware resources to userspace and creating new vulnerabilities. This dilemma stems from a hardware granularity mismatch:...

Quantigence: A Multi-Agent AI Framework for Quantum Security Research

Cryptographically Relevant Quantum Computers CRQCs pose a structural threat to the global digital economy. Algorithms like Shor's factoring and Grover's search threaten to dismantle the public-key infrastructure PKI securing sovereign communications and financial transactions. While the timeline...

Transmission Integer Overflow

2017 research from Google where Tavis found that transmission suffered from various integer overflows when parsing torrent files...

Safe2Harm: Semantic Isomorphism Attacks for Jailbreaking Large Language Models

Large Language Models LLMs have demonstrated exceptional performance across various tasks, but their security vulnerabilities can be exploited by attackers to generate harmful content, causing adverse impacts across various societal domains. Most existing jailbreak methods revolve around Prompt...

Cybersecurity AI: The World's Top AI Agent for Security Capture-The-Flag (CTF)

Are Capture-the-Flag competitions obsolete? In 2025, Cybersecurity AI CAI systematically conquered some of the world's most prestigious hacking competitions, achieving Rank 1 at multiple events and consistently outperforming thousands of human teams. Across five major circuits-HTB's AI vs Humans,...

Learning the Wrong Lessons: Syntactic-Domain Spurious Correlations in Language Models

Whitepaper from researchers at MIT, Northeastern University, and Meta. For an LLM to correctly respond to an instruction it must understand both the semantics and the domain i.e., subject area of a given task-instruction pair. However, syntax can also convey implicit information Recent work shows...

An Empirical Study on the Security Vulnerabilities of GPTs

Equipped with various tools and knowledge, GPTs, one kind of customized AI agents based on OpenAI's large language models, have illustrated great potential in many fields, such as writing, research, and programming. Today, the number of GPTs has reached three millions, with the range of specific...

From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection

Advanced Persistent Threats APT pose a major cybersecurity challenge due to their stealth, persistence, and adaptability. Traditional machine learning detectors struggle with class imbalance, high dimensional features, and scarce real world traces. They often lack transferability-performing well ...

Netscaler / Citrix ADC / Gateway Memory Overflow

This is a multi-host, multi-port scanner and auditor for CVE-2025-6543-affected NetScaler devices. Supports SNMP and SSH enumeration with optional CSV reporting and exploit stubs...

Cross-LLM Generalization of Behavioral Backdoor Detection in AI Agent Supply Chains

As AI agents become integral to enterprise workflows, their reliance on shared tool libraries and pre-trained components creates significant supply chain vulnerabilities. While previous work has demonstrated behavioral backdoor detection within individual LLM architectures, the critical question ...

Accuracy and Efficiency Trade-Offs in LLM-Based Malware Detection and Explanation: A Comparative Study of Parameter Tuning Vs. Full Fine-Tuning

This study examines whether Low-Rank Adaptation LoRA fine-tuned Large Language Models LLMs can approximate the performance of fully fine-tuned models in generating human-interpretable decisions and explanations for malware classification. Achieving trustworthy malware detection, particularly when...

Zero-Trust Strategies for O-RAN Cellular Networks: Principles, Challenges and Research Directions

Cellular networks have become foundational to modern communication, supporting a broad range of applications, from civilian use to enterprise systems and military tactical networks. The advent of fifth-generation and beyond cellular networks B5G introduces emerging compute capabilities into the...

Federated Anomaly Detection and Mitigation for EV Charging Forecasting under Cyberattacks

Electric Vehicle EV charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid stability. Existing forecasting techniques are limited by the lack of combined robust anomaly mitigation solutions and data privacy preservation...

Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks

The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication protocols that expose vast attack surfaces around both individual and network-wide safety. Existing security schemes...

ProxyPrints: From Database Breach to Spoof, a Plug-And-Play Defense for Biometric Systems

Fingerprint recognition systems are widely deployed for authentication and forensic applications, but the security of stored fingerprint data remains a critical vulnerability. While many systems avoid storing raw fingerprint images in favor of minutiae-based templates, recent research shows that...

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...

Robustness of LLM-Enabled Vehicle Trajectory Prediction under Data Security Threats

The integration of large language models LLMs into automated driving systems has opened new possibilities for reasoning and decision-making by transforming complex driving contexts into language-understandable representations. Recent studies demonstrate that fine-tuned LLMs can accurately predict...

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...

SecTracer: A Framework for Uncovering the Root Causes of Network Intrusions Via Security Provenance

Modern enterprise networks comprise diverse and heterogeneous systems that support a wide range of services, making it challenging for administrators to track and analyze sophisticated attacks such as advanced persistent threats APTs, which often exploit multiple vectors. To address this challeng...

Wapiti Web Application Vulnerability Scanner 3.2.9 Source Code

Wapiti is a web application vulnerability scanner. It will scan the web pages of a deployed web application and will fuzz the URL parameters and forms to find common web vulnerabilities. This is the source code release...

Smartphone User Fingerprinting on Wireless Traffic

Due to the openness of the wireless medium, smartphone users are susceptible to user privacy attacks, where user privacy information is inferred from encrypted Wi-Fi wireless traffic. Existing attacks are limited to recognizing mobile apps and their actions and cannot infer the smartphone user...