GNUnet P2P Framework 0.26.2

GNUnet is a peer-to-peer framework with focus on providing security. All peer-to-peer messages in the network are confidential and authenticated. The framework provides a transport abstraction layer and can currently encapsulate the network traffic in UDP IPv4 and IPv6, TCP IPv4 and IPv6, HTTP, o...

HACK THE PLANET - a Terminal Extravaganza

A full-screen Textual TUI that mashes up the neon swagger of Hackers 1995, the grounded terminal realism of Mr. Robot, and the green digital rain of The Matrix...

IServ Schoolserver User Enumeration

IServ Schoolserver suffers from a user enumeration vulnerability. The vendor does not feel this is an issue...

Module-Lattice-Based Digital Signature Algorithm Utility 20260908

This is mldsa, a module-lattice-based digital signature algorithm utility for generating keypairs, signing and verifying files and data. It has an alter ego, mlsignify, implementing the same command line switches as signify1. The mldsa and mlsignify utilities are based on the libmldsa3 library,...

CISA: Critical Manufacturing Sector Profile

This resource provides a graphical overview of the components and main segments of the Critical Manufacturing Sector...

cPanel EmailTrack CVE-2026-67401 Scanner / Mitigation

This toolkit provides detection and mitigation utilities for CVE-2026-67401, a SQL injection vulnerability affecting the cPanel and WHM EmailTrack functionality. The included scanner checks the installed cPanel version and package integrity, searches system and database logs for suspicious...

CISA: Commercial Facilities Sector Profile

This resource provides a graphical overview of the components and main segments of the Commercial Facilities Sector...

Joern 4.0.624

Joern is the bug hunter's workbench. With this tool, you can uncover attack surface, sloppy coding practices, and variants of known vulnerabilities using an interactive code analysis shell. Joern supports C, C++, LLVM bitcode, x86 binaries via Ghidra, JVM bytecode via Soot, and Javascript...

CodeQL 2.27.0

Discover vulnerabilities across a codebase with CodeQL, an industry-leading semantic code analysis engine. CodeQL lets you query code as though it were data. Write a query to find all variants of a vulnerability, eradicating it forever. Then share your query to help others do the same...

Flounder 0.4.0

Flounder turns modern coding agents into an end-to-end security audit system. Give it an authorized target boundary - a repository, source tree, package, deployed clue, or prior run - and the agent can prepare the workspace, read the code and supporting material, map the attack surface, dig into...

CISA: Logging Reference Architecture Updated

The Cybersecurity and Infrastructure Security Agency CISA developed the Logging Reference Architecture LRA in alignment with Office of Management and Budget Memorandum M-26-14: Ensuring Effective and Efficient Agency Logging and Network Visibility to Defend Against Evolving Cyber Threats, which w...

Joern 4.0.623

Joern is the bug hunter's workbench. With this tool, you can uncover attack surface, sloppy coding practices, and variants of known vulnerabilities using an interactive code analysis shell. Joern supports C, C++, LLVM bitcode, x86 binaries via Ghidra, JVM bytecode via Soot, and Javascript...

Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent-Memory Systems

Long-running language-model agents depend on persistent memory. Many agent-memory systems preserve history through soft revocation: a contradicted fact is marked invalid and retained rather than deleted. However, whether that mark is enforced at retrieval time is unexamined. In this paper, we...

NERVE Attacks: Breaking AI-Powered Brain-Computer Interfaces

The rapid integration of AI into human-centred systems such as Brain-Computer Interfaces BCIs has created a poorly understood attack surface linking neural signals to physical systems. Exploits in this domain threaten cognitive autonomy, mental privacy, and physical safety, from neural data...

Windows Malware Detector As a Compound AI System: Trade-Offs in Accuracy, Efficiency, and Adversarial Robustness

Industrial Windows malware detectors are commonly described as Compound AI Systems composed of multiple heterogeneous components, including rule-based mechanisms as well as machine-learning-based static and dynamic analyses. However, due to industrial secrecy and limited public disclosure, the...

Measuring the Security of the Evolving Software Supply Chain: A Research Agenda

Software supply chain security has become increasingly critical due to the widespread reliance on third-party dependencies and the growing attack surface of modern software ecosystems. However, existing quantitative, measurement-based analysis and vulnerability management approaches remain largel...

Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS

Network intrusion detection systems NIDS are critical for cybersecurity, safeguarding services and data from potential attacks. However, existing AI-based NIDS often assume static data distributions and fail to handle concept drift, leading to degraded performance and increased false positives in...

MemSentry: A Framework for Detecting Persistent Memory Poisoning in Agentic AI

Agentic AI systems with persistent memory introduce a distinct attack surface known as memory poisoning, in which adversarially crafted content is stored in long-term memory and subsequently influences future agent behavior. Such attacks can suppress security alerts, facilitate privilege...

ACEA: An Adversarial Co-Evolution Arena for Head-To-Head Red-Team and Blue-Team LLM Testing

Automated red-team attacks and blue-team defenses for large language models LLMs are advancing quickly. However, attackers and defenders are built and tested in isolation, and the resulting scores are hard to trust. To tackle this, we present ACEA Adversarial Co-Evolution Arena, a platform that...

Benchmark Scores Are Pipeline-Dependent: A Reliability Audit of Cybersecurity LLM Benchmarks

Large language model LLM benchmarks are often treated as fixed datasets with stable scores, yet their outcomes depend on configurable evaluation pipelines. We audit eight cybersecurity benchmarks across 10 proprietary, open-weight, and cybersecurity-specialized LLMs. By modeling benchmarks as...

Trapdoor Functions with Secure Key Leasing and Copy Protection

Inspired by the no-cloning theorem in quantum theory, a variety of quantum cryptographic primitives with unclonable functionalities, such as secure key leasing and copy protection, have been proposed and attracted significant attention. However, trapdoor functions TDFs, fundamental primitives in...

HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving

We introduce HoneyRoute, an inference-serving layer that detects whether an incoming request is malicious and, if so, routes it to a dedicated honeypot model, shielding production while the adversary's interaction is continuously harvested for intelligence. Existing defenses embed traps inside...

Evidence-Grounded Retrieval for Investigation Hunt Lead Generation from CTI Reports

Threat hunting increasingly depends on converting unstructured knowledge e.g., Cyber Threat Intelligence reports into actionable hunt leads: concise, investigable hypotheses grounded in observable artifacts and adversary techniques. Producing such leads manually is a tedious and hard-to-scale tas...

Towards Standardized Evaluation of GPU Memory Safety with GMSBench

As GPUs become increasingly integral to high-performance computing and machine learning, ensuring memory safety in GPU programs has become crucial for reliable and secure execution. However, evaluating GPU memory safety techniques remains challenging due to the lack of comprehensive and...

5GDescrambler: Locating, Descrambling, and Decoding 5G Scheduling Information (Long Version)

Tracking users in 5G NR has recently been successfully demonstrated by exploiting various side-channels. This allows for identification of individuals, classification of user activity in real time as well as tracking by fingerprinting, affecting billions of users with a 5G subscription and...

Grid Trouble in Paradise: Uncovering Vulnerable Distributed Energy Resources and Their Grid-Level Risks

Grid-connected solar distributed energy resources DERs, such as solar inverters and monitoring platforms, have been deployed at unprecedented scale over the past few years, with global solar capacity more than doubling since 2022. To support monitoring and control, many of these systems are...

Merging Cyber Threat Intelligence through Retrieval-Augmented Generation and Small Language Models for Rich Threat Representation

Modern cybersecurity operations rely on CTI collected from heterogeneous sources, including semi-structured threat representations, IoCs, and narrative technical reports. However, these artifacts are often insufficient in isolation to reconstruct how an attack unfolds, under which conditions each...

Fine-Grained Distributed Backdoor Attacks in Federated Learning

Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to centralized attacks, distributed backdoor attacks are more harmful but require more poisoned samples to compensate for the loss of trigger strength due t...

EventSpec: Defining and Detecting Event-Semantic Issues in Blockchain Ecosystems

In recent years, smart contracts have become the backbone of decentralized applications DApps, and off-chain systems such as bridges, wallets, and indexers rely heavily on event logs to track contract execution and state changes. However, the Ethereum Virtual Machine EVM does not validate or...

Enhancing Privacy, Neglecting Harms: An Analysis of Real-World Digital Privacy Incidents

Privacy-enhancing technologies PETs have emerged as a technical means for providing individuals with greater control over their information. Yet despite the growing deployment of PETs, people continue to experience privacy harms. In this work, we revisit our understanding of privacy incidents and...

Frequency-Domain Mixing Data Augmentation for Malicious Traffic Detection

The strong dynamics of network traffic often force malicious traffic detection models to handle out-of-distribution data. Typically, deep learning-based malicious traffic detection models require a large amount of high-quality training data. However, owing to challenges such as high labeling...

TrojanWorld: Backdooring World-Model Agents Via Imagination Steering

World models increasingly serve as the predictive core of model-based reinforcement learning agents, enabling them to simulate future dynamics and reason over imagined trajectories before acting. Their substantial training demands make pretrained world models attractive for distribution and reuse...

libpcap 1.10.7

Libpcap is a portable packet capture library which is used in many packet sniffers, including tcpdump...

Joern 4.0.621

Joern is the bug hunter's workbench. With this tool, you can uncover attack surface, sloppy coding practices, and variants of known vulnerabilities using an interactive code analysis shell. Joern supports C, C++, LLVM bitcode, x86 binaries via Ghidra, JVM bytecode via Soot, and Javascript...

Crossing the Streams: SSH Plaintext Recovery Via a Common Compression Context in Multiplexed Channels

SSH is the standard protocol for secure remote administration of servers. At the transport layer, SSH uses the Binary Packet Protocol BPP for encrypted and authenticated communication. Above this, the SSH Connection Protocol multiplexes one or more logical channels over a single connection,...

Staying on the Attack Path: Structured State for Long-Horizon Automated Penetration Testing

Large language model LLM based agents are increasingly applied to cybersecurity tasks such as vulnerability discovery and automated penetration testing. On long-horizon security tasks, however, such agents remain limited by context forgetting and intent drift: early critical facts and causal...

Benchmarking LLMs for Threat Level Determination

The fast progress of large language models LLMs opens new opportunities in the management of cyber threat intelligence, but their reliability for operational tasks remains unclear. In this work, we benchmark LLMs on the task of threat level determination. First, we construct a curated dataset...

One Is Not Enough: The Untold Story of Multiple Security Patches for One Vulnerability

Security patches SPs are the main mechanism for fixing software vulnerabilities, yet a single vulnerability is not always resolved by a single patch: fixes may be completed incrementally, propagated across maintained branches, or replicated across related repositories. When patch records are...

A Novel Steganography Scheme Using Quantum Hilbert Transform

The main goal of steganography is to transmit hidden messages in legitimate-looking communication messages. Phase-domain information hiding, however, has not been fully explored for quantum systems. This work introduces a finite-dimensional Quantum Hilbert Transform QHT as a unitary phase operato...

VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing defenses such as GitHub Dependabot often raise many false alerts because their coarse-grained matching cannot determine whether a vulnerable dependen...

Construction and Natural Language Querying of a Cybersecurity Knowledge Graph

Cybersecurity vulnerability information is distributed across numerous platforms and databases, making it difficult for researchers and practitioners to obtain a unified and structured understanding of existing threats. This is a critical issue in cybersecurity, where timely access to accurate...

Do AI Coding Assistants Check Before They Install? A Pre-Registered Demand-Side Audit of Trust Signals in the Research Software Supply Chain

AI coding assistants now select, install, and configure software, and attackers have exploited that position through invented package names, compromised maintainer accounts, and manipulated repository text. In response, the supply-chain community publishes machine-checkable trust signals: softwar...

Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection

Anomaly-based intrusion detection systems in industrial control systems ICS and operational technology OT environments are increasingly required to meet formal resilience criteria: absorbed adversarial disturbances, graceful degradation under sustained attack, and certified system-level guarantee...

LLM-Based Penetration Testing in the Presence of Honeypots

Large language model LLM agents are increasingly employed for offensive cybersecurity tasks such as automated vulnerability discovery, reconnaissance, and penetration testing. This new capability also threatens one of the defender's most valuable tools: deception. Traditional honeypots rely on...

AVP-Inspect: Coordinated Cyber-Physical Testing for Privacy Analysis of COTS Apple Vision Pro Applications

XR devices introduce substantial privacy concerns due to their comprehensive data collection capabilities that surpass traditional computing platforms. While existing works have demonstrated privacy concerns on Android-based XR devices such as Meta Quest series by performing network traffic...

AURA-Eval: Evaluation Framework for Acting under Risk Awareness in LLM Agent Trajectories

LLM agents operate in workflows where unsafe actions can have real consequences. Existing safety evaluations often reduce behavior to a single score, obscuring risk recognition, pre-action detection, and safe task completion when a safe solution exists. We introduce AURA-Eval, a framework combini...

MOLE: Detecting Insider Threats in AI Agents

Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model weights, poison training data, or weaken release gates. Existing benchmarks do not test whether defenders can detect this activity among routine work under a limited...

WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic

Web Application Firewalls WAFs mainly rely on signatures to detect known attacks, which can leave gaps against modified or previously unseen payloads. Positive security provides a complementary approach by learning legitimate traffic and blocking inputs that fall outside the learned profile...

AgentDrift: A Step-Labeled Benchmark of Injection-Hijacked LLM Agent Trajectories

LLM agents complete tasks by issuing sequences of tool calls, and every observation they read is a channel through which an indirect prompt injection can enter. A successful injection has a characteristic shape when the trajectory is read in order: a benign prefix gives way to actions that serve...

Lightweight Detection of Electromagnetic Signal Injection Attacks on Image Sensors

Electromagnetic signal injection attacks ESIA pose a growing threat to image sensors, which are increasingly used in different intelligent systems. By emitting electromagnetic interference, adversaries can manipulate pixel values, potentially misleading downstream artificial intelligence AI model...