4694 matches found
EUVD-2025-208717
Raytha CMS does not have any brute force protection mechanism implemented. It allows an attacker to send multiple automated logon requests without triggering lockout, throttling, or step-up challenges. This issue was fixed in version 1.4.6...
CVE-2025-69246
Raytha CMS does not have any brute force protection mechanism implemented. It allows an attacker to send multiple automated logon requests without triggering lockout, throttling, or step-up challenges. This issue was fixed in version 1.4.6...
CVE-2025-69246 Lack of bruteforce protection in Raytha CMS
Raytha CMS does not have any brute force protection mechanism implemented. It allows an attacker to send multiple automated logon requests without triggering lockout, throttling, or step-up challenges. This issue was fixed in version 1.4.6...
CVE-2025-69246
Raytha CMS (CVE-2025-69246) is affected by a lack of brute-force protection in login, allowing automated multiple logon attempts. The issue is addressed in version 1.4.6; users should upgrade to mitigate risk. If upgrading is not feasible, apply any provided workaround or vendor guidance (not det...
CVE-2025-69246 Lack of bruteforce protection in Raytha CMS
Raytha CMS does not have any brute force protection mechanism implemented. It allows an attacker to send multiple automated logon requests without triggering lockout, throttling, or step-up challenges. This issue was fixed in version 1.4.6...
Exploit for Path Traversal in Apache Http_Server
Apache 2.4.49 Path Traversal Lab — CVE-2021-41773 Clone...
Raytha CMS 安全漏洞
Raytha CMS is a content management system developed by the American company Raytha. Versions of Raytha CMS prior to 1.4.6 contained security vulnerabilities. These vulnerabilities stemmed from the lack of any brute-force attack protection mechanisms, allowing attackers to send multiple automated...
PT-2026-25698
Raytha CMS does not have any brute force protection mechanism implemented. It allows an attacker to send multiple automated logon requests without triggering lockout, throttling, or step-up challenges. This issue was fixed in version 1.4.6...
Rxss-Scan
Rxss-Scan is a lightwe...
CVE-2025-70129
If the anti spam-captcha functionality in PluXml versions 5.8.22 and earlier is enabled, a captcha challenge is generated with a format that can be automatically recognized for articles, such that an automated script is able to solve this anti-spam mechanism trivially and publish spam comments. T...
Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services
The rapid adoption of large language models LLMs in financial services introduces new operational, regulatory, and security risks. Yet most red-teaming benchmarks remain domain-agnostic and fail to capture failure modes specific to regulated BFSI settings, where harmful behavior can be elicited...
CVE-2025-70129
If the anti spam-captcha functionality in PluXml versions 5.8.22 and earlier is enabled, a captcha challenge is generated with a format that can be automatically recognized for articles, such that an automated script is able to solve this anti-spam mechanism trivially and publish spam comments. T...
CVE-2025-70129
If the anti spam-captcha functionality in PluXml versions 5.8.22 and earlier is enabled, a captcha challenge is generated with a format that can be automatically recognized for articles, such that an automated script is able to solve this anti-spam mechanism trivially and publish spam comments. T...
CVE-2025-70129
If the anti spam-captcha functionality in PluXml versions 5.8.22 and earlier is enabled, a captcha challenge is generated with a format that can be automatically recognized for articles, such that an automated script is able to solve this anti-spam mechanism trivially and publish spam comments. T...
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...
Coverage-Guided Multi-Agent Harness Generation for Java Library Fuzzing
Coverage-guided fuzzing has proven effective for software testing, but targeting library code requires specialized fuzz harnesses that translate fuzzer-generated inputs into valid API invocations. Manual harness creation is time-consuming and requires deep understanding of API semantics,...
prima-incident-response-security-poc
DevOps Security Pipeline POC A security-integrated CI/CD pipe...
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...
Supporting Artifact Evaluation with LLMs: A Study with Published Security Research Papers
Artifact Evaluation AE is essential for ensuring the transparency and reliability of research, closing the gap between exploratory work and real-world deployment is particularly important in cybersecurity, particularly in IoT and CPSs, where large-scale, heterogeneous, and privacy-sensitive data...