665 matches found
ai.timefold.solver:timefold-solver-quarkus-benchmark-integration-test (>=0.9.38 <=1.20.1), ai.timefold.solver:timefold-solver-quarkus-devui-integration-test (>=0.9.38 <=1.20.1) +1591 more potentially affected by CVE-2026-39852 via io.quarkus:quarkus-vertx-http (>=3.0.0.Alpha1 <=3.20.6)
io.quarkus:quarkus-vertx-http MAVEN version =3.0.0.Alpha1, =0.9.38, =0.9.38, =0.9.38, =0.9.38, =0.9.38, =0.9.38, =0.0.1, =0.0.1, =0.0.1, =0.0.4, =0.0.4, =0.0.4, =0.0.4, =0.0.2, =0.0.1, =0.0.5 and more Source cves: CVE-2026-39852 Source advisory: SNYK:JAVA-IOQUARKUS-16420254...
VulKey: Automated Vulnerability Repair Guided by Domain-Specific Repair Patterns
The increasing prevalence of software vulnerabilities highlights the need for effective Automatic Vulnerability Repair AVR tools. While LLM-based approaches are promising, they struggle to incorporate structured security knowledge from sources like CWE and NVD. Current methods either use this...
CVE-2026-7510
A vulnerability was determined in OWAP DefectDojo up to 2.55.4. Affected by this vulnerability is an unknown functionality of the component Benchmark/Engagement/Product/Survey. Executing a manipulation can lead to authorization bypass. The attack can be executed remotely. The exploit has been...
CVE-2026-7510 OWAP DefectDojo Benchmark/Engagement/Product/Survey authorization
A vulnerability was determined in OWAP DefectDojo up to 2.55.4. Affected by this vulnerability is an unknown functionality of the component Benchmark/Engagement/Product/Survey. Executing a manipulation can lead to authorization bypass. The attack can be executed remotely. The exploit has been...
CVE-2026-7510
The CVE-2026-7510 entry concerns OWAP DefectDojo up to 2.55.4, with an authorization bypass affecting the Benchmark/Engagement/Product/Survey functionality. The issue is reachable remotely and is supported by a public disclosure; upgrading to DefectDojo 2.56.0 addresses the vulnerability (patch e...
CVE-2026-7510 OWAP DefectDojo Benchmark/Engagement/Product/Survey authorization
A vulnerability was determined in OWAP DefectDojo up to 2.55.4. Affected by this vulnerability is an unknown functionality of the component Benchmark/Engagement/Product/Survey. Executing a manipulation can lead to authorization bypass. The attack can be executed remotely. The exploit has been...
EUVD-2026-26457
A vulnerability was determined in OWAP DefectDojo up to 2.55.4. Affected by this vulnerability is an unknown functionality of the component Benchmark/Engagement/Product/Survey. Executing a manipulation can lead to authorization bypass. The attack can be executed remotely. The exploit has been...
CVE-2026-7510 OWAP DefectDojo Benchmark/Engagement/Product/Survey authorization
A vulnerability was determined in OWAP DefectDojo up to 2.55.4. Affected by this vulnerability is an unknown functionality of the component Benchmark/Engagement/Product/Survey. Executing a manipulation can lead to authorization bypass. The attack can be executed remotely. The exploit has been...
DefectDojo 授权问题漏洞
DefectDojo is an application security and vulnerability management tool developed by DefectDojo. Versions of DefectDojo 2.55.4 and earlier contained a vulnerability related to authorization. This vulnerability stemmed from unknown functions within the Benchmark/Engagement/Product/Survey component...
PT-2026-36213
Name of the Vulnerable Software and Affected Versions OWAP DefectDojo versions prior to 2.56.0 Description An issue exists in the Benchmark, Engagement, Product, and Survey components where a manipulation can lead to a remote authorization bypass, allowing an attacker to circumvent access control...
BinExploit-Bench
BinExploit-Bench: Binary Exploitation Capability Benchmark for...
Evaluating Jailbreaking Vulnerabilities in LLMs Deployed As Assistants for Smart Grid Operations: A Benchmark against NERC Standards
The deployment of Large Language Models LLMs as assistants in electric grid operations promises to streamline compliance and decision-making but exposes new vulnerabilities to prompt-based adversarial attacks. This paper evaluates the risk of jailbreaking LLMs, i.e., circumventing safety alignmen...
CrossCommitVuln-Bench: A Dataset of Multi-Commit Python Vulnerabilities Invisible to Per-Commit Static Analysis
We present CrossCommitVuln-Bench, a curated benchmark of 15 real-world Python vulnerabilities CVEs in which the exploitable condition was introduced across multiple commits - each individually benign to per-commit static analysis - but collectively critical. We manually annotate each CVE with its...
AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models
Automated red-teaming methods for large language models typically optimize attack prompts within a fixed, human-designed strategy, leaving the attack strategy itself unchanged. We instead optimize the strategy. We propose AutoRISE, a method that searches over executable attack programs rather tha...
Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs
Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring. However, insecure logging practices can inadvertently expose sensitive information or enable attacks such as log injection, posing serious threats to...
Cyber Defense Benchmark: Agentic Threat Hunting Evaluation for LLMs in SecOps
We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model LLM agents perform the core SOC analyst task of threat hunting: given a database of raw Windows event logs with no guided questions or hints, identify the exact timestamps of malicious events. The...
Owner-Harm: A Missing Threat Model for AI Agent Safety
Existing AI agent safety benchmarks focus on generic criminal harm cybercrime, harassment, weapon synthesis, leaving a systematic blind spot for a distinct and commercially consequential threat category: agents harming their own deployers. Real-world incidents illustrate the gap: Slack AI...
InduGuard_vul_poc
ICS Vulnerability PoC Library — SCAA Benchmark Support Proof-...
SDLLMFuzz: Dynamic-Static LLM-Assisted Greybox Fuzzing for Structured Input Programs
Fuzzing has become a widely adopted technique for vulnerability discovery, yet it remains ineffective for structured-input programs due to strict syntactic constraints and limited semantic awareness. Traditional greybox fuzzers rely on mutation-based strategies and coarse-grained coverage feedbac...