Dynamic Python Payload Encryption Framework with Loader Generator

This Python program implements a small payload framework designed to encrypt, decrypt, analyze, and execute Python code using layered encoding and obfuscation techniques. It also automatically generates a self‑contained loader capable of restoring and executing the protected payload...

Incremental Federated Learning for Intrusion Detection in IoT Networks under Evolving Threat Landscape

The expansion of Internet of Things IoT devices has increased the attack surface of networks, necessitating a robust and adaptive intrusion detection systems. Machine learning based systems have been considered promising in enhancing the detection performance. Federated learning settings enabled ...

Execution Is the New Attack Surface: Survivability-Aware Agentic Crypto Trading with OpenClaw-Style Local Executors

OpenClaw-style agent stacks turn language into privileged execution: LLM intents flow through tool interception, policy gates, and a local executor. In parallel, skill marketplaces such as skills.sh make capability acquisition as easy as installing skills and CLIs, creating a growing capability...

CLIOPATRA: Extracting Private Information from LLM Insights

As AI assistants become widely used, privacy-aware platforms like Anthropic's Clio have been introduced to generate insights from real-world AI use. Clio's privacy protections rely on layering multiple heuristic techniques together, including PII redaction, clustering, filtering, and LLM-based...

Why LLMs Fail: A Failure Analysis and Partial Success Measurement for Automated Security Patch Generation

Large Language Models LLMs show promise for Automated Program Repair APR, yet their effectiveness on security vulnerabilities remains poorly characterized. This study analyzes 319 LLM-generated security patchesacross 64 Java vulnerabilities from the Vul4J benchmark. Using tri-axis evaluation...

WebDAV Advanced Penetration Testing Script

This Python-based WebDAV penetration testing script tests methods available, attempts directory listing with PROPFIND, file upload with PUT, and more...

SoK: Harmonizing Attack Graphs and Intrusion Detection Systems

Detecting and responding to cyber attacks is increasingly difficult as high-volume, complex network traffic allows threats to remain concealed. While Intrusion Detection Systems IDSs identify anomalous behavior, Attack Graphs AGs serve as the primary threat model for analyzing attacker strategies...

OSS-CRS: Liberating AIxCC Cyber Reasoning Systems for Real-World Open-Source Security

DARPA's AI Cyber Challenge AIxCC showed that cyber reasoning systems CRSs can go beyond vulnerability discovery to autonomously confirm and patch bugs: seven teams built such systems and open-sourced them after the competition. Yet all seven open-sourced CRSs remain largely unusable outside their...

SlowBA: An Efficiency Backdoor Attack Towards VLM-Based GUI Agents

Modern vision-language-model VLM based graphical user interface GUI agents are expected not only to execute actions accurately but also to respond to user instructions with low latency. While existing research on GUI-agent security mainly focuses on manipulating action correctness, the security...

Learning the APT Kill Chain: Temporal Reasoning over Provenance Data for Attack Stage Estimation

Advanced Persistent Threats APTs evolve through multiple stages, each exhibiting distinct temporal and structural behaviors. Accurate stage estimation is critical for enabling adaptive cyber defense. This paper presents StageFinder, a temporal graph learning framework for multi-stage attack...

PQC-LEO: An Evaluation Framework for Post-Quantum Cryptographic Algorithms

Advances in quantum computing threaten digital communication security by undermining the foundations of current public-key cryptography through Shor's quantum algorithm. This has driven the development of Post-Quantum Cryptography PQC, a new set of algorithms resistant to quantum attacks. While...

Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC under Channel Uncertainty

Integrated sensing and communication ISAC systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel...

EVMbench: Evaluating AI Agents on Smart Contract Security

Smart contracts on public blockchains now manage large amounts of value, and vulnerabilities in these systems can lead to substantial losses. As AI agents become more capable at reading, writing, and running code, it is natural to ask how well they can already navigate this landscape, both in way...

Recovery-Induced Erasure Attack on QKD Systems

Detector dead time is typically treated as a fixed parameter in quantum key distribution QKD security analyses. In practice, however, the effective recovery time of single-photon avalanche photodiodes SPADs depends on the incident count rate. In this work, we demonstrate that this...

A Systematic Study of LLM-Based Architectures for Automated Patching

Large language models LLMs have shown promise for automated patching, but their effectiveness depends strongly on how they are integrated into patching systems. While prior work explores prompting strategies and individual agent designs, the field lacks a systematic comparison of patching...

Exploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection

Cross-domain intrusion detection remains a critical challenge due to significant variability in network traffic characteristics and feature distributions across environments. This study evaluates the transferability of three widely used flow-based feature sets Argus, Zeek and CICFlowMeter across...

ThreatFormer-IDS: Robust Transformer Intrusion Detection with Zero-Day Generalization and Explainable Attribution

Intrusion detection in IoT and industrial networks requires models that can detect rare attacks at low false-positive rates while remaining reliable under evolving traffic and limited labels. Existing IDS solutions often report strong in-distribution accuracy, but they may degrade when evaluated ...

A Lightweight Defense Mechanism against Next Generation of Phishing Emails Using Distilled Attention-Augmented BiLSTM

The current generation of large language models produces sophisticated social-engineering content that bypasses standard text screening systems in business communication platforms. Our proposed solution for mail gateway and endpoint deception detection operates in a privacy-protective manner whil...

FreeBSD Security Advisory - FreeBSD-SA-26:04.jail

FreeBSD Security Advisory - If two sibling jails are restricted to separate filesystem trees, which is to say that neither of the two jail root directories is an ancestor of the other, jailed processes may nonetheless be able to access a shared directory via a nullfs mount, if the administrator h...

Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks

LLM agents are evolving rapidly, powered by code execution, tools, and the recently introduced agent skills feature. Skills allow users to extend LLM applications with specialized third-party code, knowledge, and instructions. Although this can extend agent capabilities to new domains, it creates...

An Explainable Memory Forensics Approach for Malware Analysis

Memory forensics is an effective methodology for analyzing living-off-the-land malware, including threats that employ evasion, obfuscation, anti-analysis, and steganographic techniques. By capturing volatile system state, memory analysis enables the recovery of transient artifacts such as decrypt...

On the Variability of Source Code in Maven Package Rebuilds

Rebuilding packages from open source is a common practice to improve the security of software supply chains, and is now done at an industrial scale. The basic principle is to acquire the source code used to build a package published in a repository such as Maven Central for Java, rebuild the...

Detecting Cybersecurity Threats by Integrating Explainable AI with SHAP Interpretability and Strategic Data Sampling

The critical need for transparent and trustworthy machine learning in cybersecurity operations drives the development of this integrated Explainable AI XAI framework. Our methodology addresses three fundamental challenges in deploying AI for threat detection: handling massive datasets through...

LLM Scalability Risk for Agentic-AI and Model Supply Chain Security

Large Language Models LLMs & Generative AI are transforming cybersecurity, enabling both advanced defenses and new attacks. Organizations now use LLMs for threat detection, code review, and DevSecOps automation, while adversaries leverage them to produce malwares and run targeted social-engineeri...

Apache Traffic Server Host Header Stability Scanner

This is a safe verification tool designed to detect abnormal Host header handling behavior in Apache Traffic Server without triggering denial of service conditions. The tool performs controlled HTTP requests and analyzes response stability, connection behavior, and service continuity...

Python Safe TAR Scanner

This Python tool provides a scanner for TAR archives, designed to detect unsafe or malicious entries before extraction...

PenTiDef: Enhancing Privacy and Robustness in Decentralized Federated Intrusion Detection Systems against Poisoning Attacks

The increasing deployment of Federated Learning FL in Intrusion Detection Systems IDS introduces new challenges related to data privacy, centralized coordination, and susceptibility to poisoning attacks. While significant research has focused on protecting traditional FL-IDS with centralized...

SigInt-Cirtanus 1.0

SigInt-Cirtanus is a Python-based, multi-threaded threat intelligence automation framework designed for defensive cybersecurity operations. It retrieves and processes real-time threat feeds e.g., from URLhaus, validates indicators, and dynamically generates detection artifacts for integration wit...

Can Adversarial Code Comments Fool AI Security Reviewers -- Large-Scale Empirical Study of Comment-Based Attacks and Defenses against LLM Code Analysis

AI-assisted code review is widely used to detect vulnerabilities before production release. Prior work shows that adversarial prompt manipulation can degrade large language model LLM performance in code generation. We test whether similar comment-based manipulation misleads LLMs during...

Nikto Web Scanner 2.6.0

Nikto is an Open Source GPL web server scanner which performs comprehensive tests against web servers for multiple items, including thousands of potentially dangerous files/programs, checks for outdated versions of over 1500 server components, and version specific problems on hundreds of servers...

motionEye 0.43.1b4 Remote Code Execution

Client-side validation in motionEye's web UI can be bypassed via overriding the JS validation function. Arbitrary values including shell interpolation syntax can be saved into the motion config. When motion is restarted, the motion process interprets the config and can execute shell syntax embedd...

glibc 2.38 Buffer Overflow

This is a local privilege escalation exploit for CVE-2023-4911, also known as "Looney Tunables", caused by a buffer overflow in the glibc dynamic loader's environment variable parsing logic. The vulnerability is triggered by crafting a maliciously long GLIBCTUNABLES string which corrupts internal...

Jailbreaking Leaves a Trace: Understanding and Detecting Jailbreak Attacks from Internal Representations of Large Language Models

Jailbreaking large language models LLMs has emerged as a critical security challenge with the widespread deployment of conversational AI systems. Adversarial users exploit these models through carefully crafted prompts to elicit restricted or unsafe outputs, a phenomenon commonly referred to as...

VulReaD: Knowledge-Graph-Guided Software Vulnerability Reasoning and Detection

Software vulnerability detection SVD is a critical challenge in modern systems. Large language models LLMs offer natural-language explanations alongside predictions, but most work focuses on binary evaluation, and explanations often lack semantic consistency with Common Weakness Enumeration CWE...

TRACE: Timely Retrieval and Alignment for Cybersecurity Knowledge Graph Construction and Expansion

The rapid evolution of cyber threats has highlighted significant gaps in security knowledge integration. Cybersecurity Knowledge Graphs CKGs relying on structured data inherently exhibit hysteresis, as the timely incorporation of rapidly evolving unstructured data remains limited, potentially...

Security Assessment of Intel TDX with Support for Live Migration

In the second and third quarters of 2025, Google collaborated with Intel to conduct a security assessment of Intel Trust Domain Extensions TDX, extending Google's previous review and covering major changes since Intel TDX Module 1.0 - namely support for Live Migration and Trusted Domain TD...

GNU Transport Layer Security Library 3.8.12

GnuTLS is a secure communications library implementing the SSL and TLS protocols and technologies around them. It provides a simple C language application programming interface API to access the secure communications protocols, as well as APIs to parse and write X.509, PKCS 12, OpenPGP, and other...

Exploring Semantic Labeling Strategies for Third-Party Cybersecurity Risk Assessment Questionnaires

Third-Party Risk Assessment TPRA is a core cybersecurity practice for evaluating suppliers against standards such as ISO/IEC 27001 and NIST. TPRA questionnaires are typically drawn from large repositories of security and compliance questions, yet tailoring assessments to organizational needs...

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity

Deep Reinforcement Learning DRL has achieved remarkable success in domains requiring sequential decision-making, motivating its application to cybersecurity problems. However, transitioning DRL from laboratory simulations to bespoke cyber environments can introduce numerous issues. This is furthe...

Empirical Evaluation of SMOTE in Android Malware Detection with Machine Learning: Challenges and Performance in CICMalDroid 2020

Malware, malicious software designed to damage computer systems and perpetrate scams, is proliferating at an alarming rate, with thousands of new threats emerging daily. Android devices, prevalent in smartphones, smartwatches, tablets, and IoTs, represent a vast attack surface, making malware...

XMap: Fast Internet-Wide IPv4 and IPv6 Network Scanner

XMap is an open-source network scanner designed for performing fast Internet-wide IPv4 and IPv6 network research scanning. XMap was initially developed as the research artifact of a paper published at 2021 IEEE/IFIP International Conference on Dependable Systems and Networks DSN '21 and then made...

Rethinking Latency Denial-Of-Service: Attacking the LLM Serving Framework, Not the Model

Large Language Models face an emerging and critical threat known as latency attacks. Because LLM inference is inherently expensive, even modest slowdowns can translate into substantial operating costs and severe availability risks. Recently, a growing body of research has focused on algorithmic...

Jamming Attacks on the Random Access Channel in 5G and B5G Networks

Random Access Channel RACH jamming poses a critical security threat to 5G and beyond B5G networks. This paper presents an analytical model for predicting the impact of Msg1 jamming attacks on RACH performance. We use the OpenAirInterface OAI open-source user equipment UE to implement a Msg1 jammi...

YARA-X 1.13.0

YARA-X is a re-incarnation of YARA, a pattern matching tool designed with malware researchers in mind. This new incarnation intends to be faster, safer and more user-friendly than its predecessor. The ultimate goal of YARA-X is replacing YARA as the default pattern matching tool for malware...

Entropy Bounds Via Hypothesis Testing and Its Applications to Two-Way Key Distillation in Quantum Cryptography

Quantum key distribution QKD achieves information-theoretic security, without relying on computational assumptions, by distributing quantum states. To establish secret bits, two honest parties exploit key distillation protocols over measurement outcomes resulting after the the distribution of...

GNSS SpAmming: A Spoofing-Based GNSS Denial-Of-Service Attack

GNSSs are vulnerable to attacks of two kinds: jamming i.e. denying access to the signal and spoofing i.e. impersonating a legitimate satellite. These attacks have been extensively studied, and we have a myriad of countermeasures to mitigate them. In this paper we expose a new type of attack:...

Clouding the Mirror: Stealthy Prompt Injection Attacks Targeting LLM-Based Phishing Detection

Phishing sites continue to grow in volume and sophistication. Recent work leverages large language models LLMs to analyze URLs, HTML, and rendered content to decide whether a website is a phishing site. While these approaches are promising, LLMs are inherently vulnerable to prompt injection PI...

Post-Quantum Identity-Based TLS for 5G Service-Based Architecture and Cloud-Native Infrastructure

Cloud-native application platforms and latency-sensitive systems such as 5G Core networks rely heavily on certificate-based Public Key Infrastructure PKI and mutual TLS to secure service-to-service communication. While effective, this model introduces significant operational and performance...

Steganographic Information Hiding Via Symmetric Numerical Semigroups

We introduce a steganographic information hiding scheme based on structural properties of numerical semigroups arising from the Frobenius coin problem. Instead of encoding data through representable integers, the proposed protocol embeds information into the gap structure of carefully chosen...

The Trigger in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers

Detecting whether a model has been poisoned is a longstanding problem in AI security. In this work, we present a practical scanner for identifying sleeper agent-style backdoors in causal language models. Our approach relies on two key findings: first, sleeper agents tend to memorize poisoning dat...