jsteg

jsteg go get lukechampine.com/jsteg jsteg is a package for hiding data inside jpeg files, a technique known as steganography. This is accomplished by copying each bit of the data into the least-significant bits of the image. The amount of data that can be hidden depends on the filesize of the jpe...

stego-toolkit

Steganography Toolkit This project is a Docker image useful for solving Steganography challenges as those you can find at CTF platforms like hackthebox.eu. The image comes pre-installed with many popular tools see list below and several screening scripts you can use check simple things for...

Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security

Ensuring the security of the power system is essential for stability and reliability, especially in the event of disruption. Effective classification of contingency in power systems enables proactive decision-making and mitigates large-scale breakdowns and failures. This study explores the use of...

Exploit for CVE-2026-76904

GeoServer WFS Inventory und autorisierte Verifikation Dieses...

Evaluating Large Language Models for Symbolic Security Protocol Analysis

Security protocol verification relies on formal tools such as ProVerif and OFMC. This study evaluates whether Large Language Models LLMs can perform comparable analysis. We test GPT and DeepSeek in chat and reasoning modes over three runs on 130 obfuscated AnB/AnBx protocols covering 388 security...

Oracle Financial Services Customer Screening Unauthorized Access

The vulnerability of the User Interface component of the Oracle Financial Services Customer Screening software lies in an incorrect authentication procedure. Exploiting this vulnerability allows a malicious actor, operating remotely, to gain unauthorized access to the device through HTTP requests...

CVE-2026-34320

Vulnerability in the Oracle Financial Services Customer Screening product of Oracle Financial Services Applications component: User Interface. The supported version that is affected is 8.1.2.8.0. Easily exploitable vulnerability allows unauthenticated attacker with network access via HTTP to...

A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information. Traditional blacklist and rule-based detection approaches are reactive and often fail to identify newly emerging phishing URLs. This paper proposes a lightweight hybrid framework f...

Measuring Real-World Prompt Injection Attacks in LLM-Based Resume Screening

LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated conceptually in academic studies or through a few anecdotal case studies. Its prevalence and impact in real-world LLM-based applications are largely unexplored. In this work, we present th...

AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey

Security alert screening is the downstream task of filtering, prioritizing, correlating, and contextualizing alerts for analyst attention in Security Operations Centers. This survey reviews artificial-intelligence-driven alert screening and alert-fatigue mitigation from 2015 to 2026. We synthesiz...

EUVD-2026-24422

Vulnerability in the Oracle Financial Services Customer Screening product of Oracle Financial Services Applications component: User Interface. The supported version that is affected is 8.1.2.8.0. Easily exploitable vulnerability allows unauthenticated attacker with network access via HTTP to...

CVE-2026-34320

Vulnerability in the Oracle Financial Services Customer Screening product of Oracle Financial Services Applications component: User Interface. The supported version that is affected is 8.1.2.8.0. Easily exploitable vulnerability allows unauthenticated attacker with network access via HTTP to...

CVE-2026-34320

Vulnerability in the Oracle Financial Services Customer Screening product of Oracle Financial Services Applications component: User Interface. The supported version that is affected is 8.1.2.8.0. Easily exploitable vulnerability allows unauthenticated attacker with network access via HTTP to...

PT-2026-34142

Name of the Vulnerable Software and Affected Versions Oracle Financial Services Customer Screening version 8.1.2.8.0 Description An issue in the User Interface component allows an unauthenticated attacker with network access via HTTP to compromise the system. This can lead to unauthorized access ...

Oracle Financial Services Customer Screening 安全漏洞

Oracle Financial Services Customer Screening is a financial customer screening and risk identification system developed by Oracle Corporation. Version 8.1.2.8.0 of Oracle Financial Services Customer Screening contains a security vulnerability. This vulnerability stems from issues with the User...

CVE-2026-34320: Improper Authorization

Vulnerability in the Oracle Financial Services Customer Screening product of Oracle Financial Services Applications component: User Interface. The supported version that is affected is 8.1.2.8.0. Easily exploitable vulnerability allows unauthenticated attacker with network access via HTTP to...

Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain

Large language model LLM agents increasingly rely on third-party API routers to dispatch tool-calling requests across multiple upstream providers. These routers operate as application-layer proxies with full plaintext access to every in-flight JSON payload, yet no provider enforces cryptographic...

VulnAgent-X: A Layered Agentic Framework for Repository-Level Vulnerability Detection

VulnAgent-X is a layered agentic framework integrating lightweight risk screening, bounded context expansion, specialized analysis agents, selective dynamic verification, and evidence fusion into a unified pipeline. Included in this archive is also a whitepaper from the researchers...

Recursive Language Models for Jailbreak Detection: A Procedural Defense for Tool-Augmented Agents

Jailbreak prompts are a practical and evolving threat to large language models LLMs, particularly in agentic systems that execute tools over untrusted content. Many attacks exploit long-context hiding, semantic camouflage, and lightweight obfuscations that can evade single-pass guardrails. We...

Understanding Security Risks of AI Agents' Dependency Updates

Package dependencies are a critical control point in modern software supply chains. Dependency changes can substantially alter a project's security posture. As AI coding agents increasingly modify software via pull requests, it is unclear whether their dependency decisions introduce distinct...