New: AI-Powered Patch Reliability Scoring—Predict Patch Impact Before You Deploy

What do advisory USN-7545-1 and Windows updates KB5065426 , KB5063878 , KB5055523 , and KB5066835 have in common? Based on anonymized Qualys telemetry from 2025, they were among the most frequently rolled-back patches , in other words, patches that had to be undone after deployment. Rollbacks...

Execution-State-Aware LLM Reasoning for Automated Proof-Of-Vulnerability Generation

Proof-of-Vulnerability PoV generation is a critical task in software security, serving as a cornerstone for vulnerability validation, false positive reduction, and patch verification. While directed fuzzing effectively drives path exploration, satisfying complex semantic constraints remains a...

AI-Generated Text and the Detection Arms Race

In 2023, the science fiction literary magazine Clarkesworld stopped accepting new submissions because so many were generated by artificial intelligence. Near as the editors could tell, many submitters pasted the magazine’s detailed story guidelines into an AI and sent in the results. And they...

SecCodePRM: A Process Reward Model for Code Security

Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging. Existing vulnerability detection pipelines either rely on static analyzers or use LLM/GNN-based detectors trained with coarse program-level...

A one-prompt attack that breaks LLM safety alignment

Large language models LLMs and diffusion models now power a wide range of applications, from document assistance to text-to-image generation, and users increasingly expect these systems to be safety-aligned by default. Yet safety alignment is only as robust as its weakest failure mode. Despite...

Persistent Human Feedback, LLMs, and Static Analyzers for Secure Code Generation and Vulnerability Detection

Existing literature heavily relies on static analysis tools to evaluate LLMs for secure code generation and vulnerability detection. We reviewed 1,080 LLM-generated code samples, built a human-validated ground-truth, and compared the outputs of two widely used static security tools, CodeQL and...

Hallucination-Resistant Security Planning with a Large Language Model

Large language models LLMs are promising tools for supporting security management tasks, such as incident response planning. However, their unreliability and tendency to hallucinate remain significant challenges. In this paper, we address these challenges by introducing a principled framework for...

Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents

Large language models LLMs have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to limited interaction, weak execution grounding, and a lack of experience reuse. We propose Co-RedTeam, a security-aware...

EUVD-2020-30930

Online-Exam-System 2015 contains a time-based blind SQL injection vulnerability in the feedback form that allows attackers to extract database password hashes. Attackers can exploit the 'feed.php' endpoint by crafting malicious payload requests that use time delays to systematically enumerate use...

EUVD-2020-30925

Online-Exam-System 2015 contains a SQL injection vulnerability in the feedback module that allows attackers to manipulate database queries through the 'fid' parameter. Attackers can inject malicious SQL code into the 'fid' parameter to potentially extract, modify, or delete database information...

CVE-2020-37057

Online-Exam-System 2015 contains a SQL injection vulnerability in the feedback module that allows attackers to manipulate database queries through the 'fid' parameter. Attackers can inject malicious SQL code into the 'fid' parameter to potentially extract, modify, or delete database information...

CVE-2020-37057

Online-Exam-System 2015 contains a SQL injection vulnerability in the feedback module that allows attackers to manipulate database queries through the 'fid' parameter. Attackers can inject malicious SQL code into the 'fid' parameter to potentially extract, modify, or delete database information...

CVE-2020-37051

Online-Exam-System 2015 contains a time-based blind SQL injection vulnerability in the feedback form that allows attackers to extract database password hashes. Attackers can exploit the 'feed.php' endpoint by crafting malicious payload requests that use time delays to systematically enumerate use...

CVE-2020-37057 Online-Exam-System 2015 - 'fid' SQL Injection

Online-Exam-System 2015 contains a SQL injection vulnerability in the feedback module that allows attackers to manipulate database queries through the 'fid' parameter. Attackers can inject malicious SQL code into the 'fid' parameter to potentially extract, modify, or delete database information...

CVE-2020-37057 Online-Exam-System 2015 - 'fid' SQL Injection

Online-Exam-System 2015 contains a SQL injection vulnerability in the feedback module that allows attackers to manipulate database queries through the 'fid' parameter. Attackers can inject malicious SQL code into the 'fid' parameter to potentially extract, modify, or delete database information...

CVE-2020-37057

Online-Exam-System 2015 contains a SQL injection vulnerability in the feedback module that allows attackers to manipulate database queries through the 'fid' parameter. Attackers can inject malicious SQL code into the 'fid' parameter to potentially extract, modify, or delete database information...

CVE-2020-37057

CVE-2020-37057 affects Online-Exam-System 2015. A SQL injection in the feedback module is exploitable via the fid parameter, enabling manipulation of database queries and potential extraction, modification, or deletion of data. The CVSS metrics indicate high impact to confidentiality, integrity, ...

CVE-2020-37051 Online-Exam-System 2015 - 'feedback' SQL Injection

Online-Exam-System 2015 contains a time-based blind SQL injection vulnerability in the feedback form that allows attackers to extract database password hashes. Attackers can exploit the 'feed.php' endpoint by crafting malicious payload requests that use time delays to systematically enumerate use...

CVE-2020-37051

Online-Exam-System 2015 contains a time-based blind SQL injection vulnerability in the feedback form that allows attackers to extract database password hashes. Attackers can exploit the 'feed.php' endpoint by crafting malicious payload requests that use time delays to systematically enumerate use...

CVE-2020-37051

CVE-2020-37051 affects the Online-Exam-System 2015. A time-based blind SQL injection in the feedback form enables attackers to extract database password hashes via the feed.php endpoint, using crafted time-delayed payloads to enumerate password characters. Reported CVSS metrics (v3.1, base score ...