Are We Shooting Flies with Cannons? Trade-Off Analysis for AI-Based 5G Intrusion Detection

The increasing adoption of Artificial Intelligence AI in network intrusion detection raises the question of whether complex and computationally expensive models are justified for this task. In this work, we investigate the trade-off between detection performance and computational cost for intrusi...

From Documentation to Zero-Day Vulnerabilities: LLM-Driven Fuzzing of JavaScript Engines in PDF Readers

Existing fuzzers for PDF readers rely on simple test cases that involve only individual API calls, leading to limited coverage and potentially missing vulnerabilities that require sequences of API calls. To address these limitations, we propose PDFuzzer, a novel PDF engine fuzzer that automatical...

AI Security Leaderboard: Methodology, Results and Minimal Standard

Frontier AI model developers increasingly rely on layered safeguards to prevent catastrophic misuse, but little public evidence exists on how much protection these safeguards provide, or how consistently across developers. We introduce the FAR.AI Minimal Standard for Safeguards, Version 1.0: a...

Trusted Credentials, Untrusted Behavior: Benchmarking LLM-Agent Security in High-Performance Computing

Large language model LLM agents are starting to take on routine work in high-performance computing HPC, including monitoring Slurm jobs, diagnosing failed builds, inspecting simulation output, and coordinating scientific workflows. To do this work, an agent commonly acts under its user's...

Anticipating Decoder Side-Channel Attacks in Fault-Tolerant Quantum Computers

As quantum computing emerges as an applied technology, there is a growing need to protect quantum computers against information security attacks. This work identifies a new class of side-channel attacks against fault-tolerant quantum computers, in which the syndrome data that is sent to the decod...

From CVE to CWE: Syscall-Based HIDS Generalisation

Host intrusion detection systems HIDS based on system-call traces are typically trained and evaluated against individual Common Vulnerabilities and Exposures CVE instances. In operational settings, however, defenders need to recognise new exploits of an already known type of weakness. We...

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets

This paper investigates a unexplored yet impactful vulnerability in AI explainability used in intrusion detection IDS: multicollinearity-induced instability. Despite extensive reliance on post-hoc explainability tools such as SHAP or LIME, the impact of correlated features on explanation robustne...

Babel: Jailbreaking Safety Attention Via Obfuscation Distribution Optimized Sampling

Despite rigorous safety alignment, Large Language Models LLMs remain vulnerable to jailbreak attacks. Existing black-box methods often rely on heuristic templates or exhaustive trials, lacking mechanistic interpretability and query efficiency. In this study, we investigate an intrinsic...

Root-Cause-Driven Automated Vulnerability Repair

Recent LLM-based systems have made automated vulnerability repair increasingly practical, but two challenges remain. First, without strong signals about where a bug originates, repair agents drift toward shallow edits that silence the observed failure while leaving the underlying defect unresolve...

LLM-Based Vulnerability Discovery through the Lens of Code Metrics

Large language models LLMs excel in many tasks of software engineering, yet progress in leveraging them for vulnerability discovery has stalled in recent years. To understand this phenomenon, we investigate LLMs through the lens of classic code metrics. Surprisingly, we find that a classifier...

FragFake: a Dataset for Fine-Grained Detection of Edited Images with Vision Language Models

Fine-grained edited image detection of localized edits in images is crucial for assessing content authenticity, especially given that modern diffusion models and image editing methods can produce highly realistic manipulations. However, this domain faces three challenges: 1 Binary classifiers yie...

Incident-Data Robustness Analysis of the OWASP Top 10 for LLM Applications (2026): How a Community-Expert Ranking Holds up against a Large-Scale LLM Incident Corpus

The OWASP Top 10 for LLM Applications ranks the risks that a community of security practitioners judges most important. We ask a narrower question: checked against the record of real incidents, does that expert ranking agree with the data? We assembled a large-scale corpus of LLM-security inciden...

AI Forensics across White-, Grey-, and Black-Box Access: A Process Model and Research Agenda for Post-Incident Investigation of AI Systems

AI systems are increasingly involved in decisions and actions that may later require investigation. When an AI related incident occurs, investigators need to reconstruct what the system did, why it behaved that way, and which part of the system or supply chain contributed to the outcome. Existing...

AgentAntibody: An Adaptive Immune System for Defending LLM Agents against Prompt Injection

Prompt injection remains a critical threat to LLM agents, yet existing defenses treat each task as a self-contained problem, independent of previous encounters. In practice, user requests are often underspecified: they describe the desired outcome without fully specifying acceptable behavior. An...

Behavioral Information Leakage in Darknet Traffic: A Multi-Channel Analysis across Anonymity Networks

Existing darknet traffic classification studies largely emphasize predictive accuracy while offering limited insight into the behavioral mechanisms that make encrypted services distinguishable. This paper proposes a behavioral information leakage framework that decomposes flow-level traffic into...

Between Safe Boundaries: Exploiting Temporal Consistency for Jailbreaking Text-To-Video Generation Models

Recently, text-to-video T2V models have been widely deployed, sparking growing concerns over their robustness against jailbreak attacks. Existing jailbreak methods, mostly adapted from text-to-image attacks, suffer notable drawbacks when applied to T2V systems. They fail to fully leverage tempora...

Prezta: Provable Remote Execution of Zero-Trust Authorization Using SNARKs

Modernizing the security of operational technology systems that control critical infrastructure has become a pressing challenge. Because edge devices have limited capabilities, modernization has relied on application gateways that interface with identity management systems and enforce access...

Measurement Study of Post-Quantum Readiness of Internet: 2026

The emergence of quantum computing presents a fundamental challenge to the security of current Internet communication systems. Transport Layer Security TLS, which forms the backbone of secure web communication, predominantly relies on classical public-key cryptographic algorithms such as RSA and...

OpenSSL Security Advisory 20260609

OpenSSL is susceptible to multiple security vulnerabilities. A specially crafted PKCS7 or S/MIME signed message could trigger a use-after-free during PKCS7 signature verification. The Cryptographic Message Services CMS processing fails to perform sufficient input validation on the cipher and tag...

YellowKey Bitlocker Bypass Mitigation

YellowKey is a zero-day physical attack vulnerability discovered in May 2026 that allows attackers with physical access to completely bypass BitLocker encryption on Windows 11 devices. This is a mitigation that modifies the Windows Recovery Environment to remove or disable the vulnerable...

How Reliable Are AI Attackers against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency

Large language models LLMs can autonomously conduct multi-stage cyber attacks, but the consistency of their offensive behavior under repeated trials remains unstudied. This work presents the first large-scale empirical measurement of LLM attack consistency: 400 autonomous penetration testing runs...

CodeQL 2.25.5

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...

Federated Naive Bayes with Real Mixture of Gaussians and Institutional Governance Regularization for Network Intrusion Detection

Federated learning for intrusion detection rests on a flawed premise: that every participating institution contributes equally to the shared model. In practice, a financial institution with mature security controls and low vulnerability exposure produces fundamentally different data than a...

Wapiti Web Application Vulnerability Scanner 3.3.0

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...

Forensic Analysis of Video Data Deletion and Recovery in Honeywell Surveillance File System

Real-time video surveillance systems store recorded video using digital video recorders DVRs and network video recorders NVRs. To support continuous high-volume video storage, these devices employ specialized, nonstandard file systems that are often proprietary and undocumented. This lack of...

WPProbe Plugin Enumeration Tool 0.11.4

A fast WordPress plugin and theme scanner that detects installed plugins via REST API enumeration and themes from HTML discovery, then maps them to known vulnerabilities. Over 5,000 plugins detectable without brute-force, thousands more with it...

Auditing MCP Servers for Over-Privileged Tool Capabilities

The Model Context Protocol MCP has emerged as a standard for connecting Large Language Models LLMs to external tools and data. However, MCP servers often expose privileged capabilities, such as file system access, network requests, and command execution that can be exploited if not properly...

Secure Wireless Communication Using Distributed Coherent Transmission and Spatial Signal Decomposition

We present a new approach to secure wireless communications using coherent distributed transmission of signals that are spatially decomposed between a two-element distributed antenna array. High-accuracy distributed coordination of microwave wireless systems supports the ability to transmit...

Hyperflex: A SIMD-Based DFA Model for Deep Packet Inspection

Deep Packet Inspection DPI has been extensively employed for network security. It examines traffic payloads by searching for regular expressions regex with the Deterministic Finite Automaton DFA model. However, as the network bandwidth and ruleset size are increasing rapidly, the conventional DFA...

Toward Cybersecurity-Expert Small Language Models

Large language models LLMs are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domain-specific models and training datasets. To address this gap, we present CyberPal 2.0, a family of cybersecurity-expert small language models SLMs ranging fr...

CoTSRF: Utilize Chain of Thought As Stealthy and Robust Fingerprint of Large Language Models

Despite providing superior performance, open-source large language models LLMs are vulnerable to abusive usage. To address this issue, recent works propose LLM fingerprinting methods to identify the specific source LLMs behind suspect applications. However, these methods fail to provide stealthy...

Packet Storm New Exploits for April, 2025

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

Red Hat Security Advisory 2026-55858-03

Red Hat Security Advisory 2026-55858-03 - An update for .NET 10.0 is now available for Red Hat Enterprise Linux 10. Issues addressed include bypass, denial of service, and information leakage vulnerabilities...

Behavioral Skill Reconstruction: Reconstructing Hidden Functionality from LLM Agent Skills

Closed source agent skills may encode proprietary instructions, scripts, constants, and data. Providers may offer their capabilities as services while keeping the underlying packages hidden. Prior work focuses on prompt injection attacks that directly disclose these artifacts, and existing defens...

CARE: Pre-Execution Command Verification for Shell-Executing LLM Agents

Large Language Model LLM agents are increasingly used for coding and terminal automation, making shell-command dispatch a high-stakes runtime control point. We study command-level pre-execution mediation for individual shell commands produced by LLM agents under bounded path context. Existing...

RRAM-DP: Device-Calibrated Differential Privacy for In-Memory Edge Learning

Edge Artificial Intelligence of Things AIoT systems often collect sensitive data in situ, raising serious privacy concerns. Resistive-switching random-access memory RRAM is an attractive substrate for efficient AIoT thanks to its multi-bit storage and compute-in-memory CiM capabilities, while its...

Rise from the Ashes: LLM-Based Static Analysis for Deep Learning Framework Bugs

Deep learning DL frameworks are critical AI infrastructures that often hide bugs with serious security implications. While dynamic approaches such as fuzzing are effective in uncovering these bugs, they require real test execution and incur high computational costs. Static analysis is a natural...

Font Generator for Embedded Bitmap and Color Glyph Pipeline Robustness Testing

This Python program constructs a handcrafted TrueType font file that combines multiple font subsystems - including embedded bitmap tables, color glyph definitions, glyph mapping structures, and minimal layout metadata - into a single synthetic test artifact...

Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code

Large Language Models LLMs are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding GCD has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this...

FORGE: Multi-Agent Graduated Exploitation and Detection Engineering

Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities proof-of-concept generation, vulnerability prioritization, and detection rule engineering operate largely in isolation. Existing automated exploit generation systems report...

Detecting Aimbot Cheaters in MOGs

Multiplayer Online Games have become a multibillion dollar industry in the entertainment sector. However, the presence of cheaters undermines the experience of honest players and devalues the effort of game developers, as it directly affects player retention, competitive integrity, the legitimacy...

The Surface You Test Is Not the Surface That Breaks

Tool-augmented LLM agents are vulnerable to prompt injection: a third party who controls part of the agent's context can plant instructions that the agent then executes as if they came from the user. Current evaluations report a single attack success rate per model on one channel, the tool output...

FALCON-C: Flow-Based Analysis and Labeling for Connected Vehicular Network Cybersecurity

Along with the recent rise in popularity of Electric Vehicles EVs, Electric Vehicle Supply Equipment EVSE has emerged as a new target for cyber attacks. Therefore, ensuring the security and integrity of network communication between EVSE components and vehicular clients is a significant challenge...

Aether Smart Contract Security Analysis Framework 5.0.2

Aether is a Python-based framework for analyzing Solidity smart contracts, generating vulnerability findings, producing Foundry-based proof-of-concept PoC tests, and validating exploits on mainnet forks. It combines Solidity AST parsing, taint analysis, control flow graph analysis, cross-contract...

A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflection

Open-source libraries are widely used in modern software development, introducing significant security vulnerabilities. While static analysis tools can identify potential vulnerabilities at scale, they often generate overwhelming reports with high false positive rates. Automated Exploit Generatio...

WhatWeb Scanner 0.6.4

WhatWeb is a next-generation web scanner. WhatWeb recognizes web technologies including content management systems CMS, blogging platforms, statistic/analytics packages, JavaScript libraries, web servers, and embedded devices. WhatWeb has over 1800 plugins, each to recognize something different...

Crypto-RV: High-Efficiency FPGA-Based RISC-V Cryptographic Co-Processor for IoT Security

Cryptographic operations are critical for securing IoT, edge computing, and autonomous systems. However, current RISC-V platforms lack efficient hardware support for comprehensive cryptographic algorithm families and post-quantum cryptography. This paper presents Crypto-RV, a RISC-V co-processor...

Gamifying Cyber Governance: A Virtual Escape Room to Transform Cybersecurity Policy Education

Serious games are gaining popularity as effective teaching and learning tools, providing engaging, interactive, and practical experiences for students. Gamified learning experiences, such as virtual escape rooms, have emerged as powerful tools in bridging theory and practice, fostering deeper...

Comparing AI Agents to Cybersecurity Professionals in Real-World Penetration Testing

We present the first comprehensive evaluation of AI agents against human cybersecurity professionals in a live enterprise environment. We evaluate ten cybersecurity professionals alongside six existing AI agents and ARTEMIS, our new agent scaffold, on a large university network consisting of 8,00...

Heracles: Chosen Plaintext Attack on AMD SEV-SNP

A whitepaper discussing an attack on AMD SEV-SNP called Heracles that was able to leak kernel memory, crypto keys, and user passwords, as well as demonstrate web session hijacking...