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...

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...

Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw

The rapid evolution of Large Language Models LLMs into autonomous, tool-calling agents has fundamentally altered the cybersecurity landscape. Frameworks like OpenClaw grant AI systems operating-system-level permissions and the autonomy to execute complex workflows. This level of access creates...

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...

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...

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...

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...

DREA: Decoupled Reasoning and Exploration Agents for Repository-Level Vulnerability Detection

Large language models LLMs are increasingly applied to vulnerability detection due to their strong code comprehension capabilities, but most existing approaches rely on isolated functions or context extracted by fixed program-analysis rules. These methods cannot adaptively explore repository-leve...

Apple Security Advisory 06-29-2026-1

Apple Security Advisory 06-29-2026-1 - iOS 26.5.2 and iPadOS 26.5.2 addresses double free, out of bounds access, out of bounds write, and use-after-free vulnerabilities...

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...

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...

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...

Quality-Diversity Evolution for Discovering Diverse Vulnerabilities in LLM Safety

Current approaches to LLM adversarial testing suffer from coverage gaps: manual red-teaming does not scale, LLM-as-attacker methods exhibit mode collapse, and gradient-based approaches produce uninterpretable gibberish. We introduce a quality-diversity evolutionary framework that operates at the...

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...

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...

Generating Proof-Of-Vulnerability Tests to Help Enhance the Security of Complex Software

Developers create modern software applications Apps on top of third-party libraries Libs. When library vulnerabilities are reachable through application code, the applications can be vulnerable to software supply chain attacks. Prior work shows that developers often require concrete and executabl...

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...

The Semantic Trap: Do Fine-Tuned LLMs Learn Vulnerability Root Cause or Just Functional Pattern?

LLMs demonstrate promising performance in software vulnerability detection after fine-tuning. However, it remains unclear whether these gains reflect a genuine understanding of vulnerability root causes or merely an exploitation of functional patterns. In this paper, we identify a critical failur...

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...

SoK: Systematizing a Decade of Architectural RowHammer Defenses through the Lens of Streaming Algorithms

A decade after its academic introduction, RowHammer RH remains a moving target that continues to challenge both the industry and academia. With its potential to serve as a critical attack vector, the ever-decreasing RH threshold now threatens DRAM process technology scaling, with a superlinearly...

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...

PromptSleuth: Detecting Prompt Injection Via Semantic Intent Invariance

Large Language Models LLMs are increasingly integrated into real-world applications, from virtual assistants to autonomous agents. However, their flexibility also introduces new attack vectors-particularly Prompt Injection PI, where adversaries manipulate model behavior through crafted inputs. As...

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...

BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems

AI agents have the potential to significantly alter the cybersecurity landscape. To help us understand this change, we introduce the first framework to capture offensive and defensive cyber-capabilities in evolving real-world systems. Instantiating this framework with BountyBench, we set up 25...

Red Hat Security Advisory 2026-55857-03

Red Hat Security Advisory 2026-55857-03 - An update for .NET 10.0 is now available for Red Hat Enterprise Linux 9. Issues addressed include bypass, denial of service, and information leakage vulnerabilities...

Apple Security Advisory 07-27-2026-4

Apple Security Advisory 07-27-2026-4 - macOS Sonoma 14.8.8 addresses buffer overflow, bypass, code execution, denial of service, double free, heap corruption, information leakage, integer overflow, out of bounds read, out of bounds write, traversal, and use-after-free vulnerabilities...

AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair

Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic AI approaches have shown promising results in automated program repair. However, vulnerability repair demands richer program context than general b...

Toward Cryptographically Verifiable Authorization for Autonomous AI Agents: A Security Hypothesis, Preliminary Formal Model, and Proof-Of-Concept Implementation

Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight. Existing authentication and authorization mechanisms establish identity and delegate authority, but do not inherently provide cryptographic evidence that a concrete...

Self-State Attacks on Self-Hosted AI Agents: How Far Can OS Defenses Go?

Self-hosted AI agents read and write their own memory and configuration files to function. An agent may get compromised via corruption of its own state -- a compromise realized via legitimate OS system call invocation. We refer to this class of threats as self-state attacks. In this paper, we...

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...

Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning

Prior research suggests that differential privacy DP inherently enhances the robustness of federated learning FL against backdoor attacks. In this paper, we challenge this assumption. Through an empirical analysis of two baseline attack strategies, we uncover a fundamental tension in DP-FL: while...

Detecting Bot Detection: Prevalence, Techniques, and Implications for Web Measurement Research

Browser automation frameworks are essential tools for security and privacy research on the web, yet bot detection scripts increasingly probe their artifacts, threatening measurement validity as automated browsers may be blocked or served different content. Prior work measures detection deployment...

Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response

Autonomous network-security response systems promise to reduce Security Operations Centre SOC reaction latency, but reward-only multi-agent reinforcement learning MARL can improve security reward while remaining non-deployable. We present a safety-contract graph MARL framework and instantiate it ...

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...

Space Fabric: A Satellite-Enhanced Trusted Execution Architecture

The emergence of decentralized satellite networks and orbital computing platforms creates a pressing need for trust architectures that can operate without physical access to the hardware, without reliance on pre-provisioned vendor secrets, and without dependence on a single manufacturer's...

Benchmarking Security Risk Detection and Verification in Open Agentic Skill Ecosystems

Open agent platforms allow community contributors to publish reusable skills that agents can invoke at runtime. This extensibility also creates a supply-chain risk: malicious contributors can hide harmful behavior inside skills that appear benign under superficial inspection. However, existing...

Token-Level Generalization in LoRA Adapter Backdoors: Attack Characterization and Behavioral Detection

We show that LoRA adapters, the dominant distribution format for fine-tuned LLMs, can be reliably backdoored through training data poisoning while preserving baseline task performance. On a Qwen 2.5 1.5B prompt-injection classifier, a small fraction of poisoned examples drives a...

Towards Cybersecurity SuperIntelligence (CSI): What'S the Best Harness for Cybersecurity?

What is the best harness for cybersecurity AI? Cybersecurity systems are converging on a single execution scaffold per agent, an iterative shell loop driven by a Large Language Model LLM. However, scaffolds are not interchangeable, rarely interoperable, and no single scaffold dominates across all...

CVE-2026-0265 Vulnerability Assessment Tool

CVE-2026-0265 is a remote authentication bypass affecting PAN-OS and Panorama that triggers when an authentication profile uses Cloud Authentication Service CAS. This tool safely detects whether an instance is vulnerable without authenticating any session or modifying any state...

DCVD: Dual-Channel Cross-Modal Fusion for Joint Vulnerability Detection and Localization

Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining whether a function is vulnerable but also pinpointing the specific lines responsible. However, existing approaches either rely on a single information source...

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...

Supply-Chain Poisoning Attacks against LLM Coding Agent Skill Ecosystems

LLM-based coding agents extend their capabilities via third-party agent skills distributed through open marketplaces without mandatory security review. Unlike traditional packages, these skills are executed as operational directives with system-level privileges, so a single malicious skill can...

MUZZLE: Adaptive Agentic Red-Teaming of Web Agents against Indirect Prompt Injection Attacks

Large language model LLM based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf. While these agents offer powerful capabilities, their design exposes them to indirect prompt injection attacks...

RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance

Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world scenarios that can amplify backdoor threats. This paper presents the first in-depth investigation of how the dataset...

Small Language Models for Phishing Website Detection: Cost, Performance, and Privacy Trade-Offs

Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructur...

Future-Proofing Cloud Security against Quantum Attacks: Risk, Transition, and Mitigation Strategies

Quantum Computing QC introduces a transformative threat to digital security, with the potential to compromise widely deployed classical cryptographic systems. This survey offers a comprehensive and systematic examination of quantumsafe security for Cloud Computing CC, focusing on the...

Civil Servants As Builders: Enabling Non-IT Staff to Develop Secure Python and R Tools

Current digital government literature focuses on professional in-house IT teams, specialized digital service teams, vendor-developed systems, or proprietary low-code/no-code tools. Almost no scholarship addresses a growing middle ground: technically skilled civil servants outside formal IT roles...

Good News for Script Kiddies? Evaluating Large Language Models for Automated Exploit Generation

Large Language Models LLMs have demonstrated remarkable capabilities in code-related tasks, raising concerns about their potential for automated exploit generation AEG. This paper presents the first systematic study on LLMs' effectiveness in AEG, evaluating both their cooperativeness and technica...