ProjectSend认证绕过漏洞(CVE-2024-11680)
CVE-2024-11680
ProjectSend认证绕过漏洞(CVE-2024-11680)
2026-04-29 归档
今日共收录 828 条安全情报，包含 0 个 CVE，482 篇安全通告，以及 32 篇研究论文。
← 返回归档列表PyTorch库RPC框架反序列化RCE漏洞(CVE-2024-48063)
CVE-2024-48063
PyTorch库RPC框架反序列化RCE漏洞(CVE-2024-48063)
Grafana认证后DuckDB-SQL注入漏洞(CVE-2024-9264)
CVE-2024-9264
Grafana认证后DuckDB-SQL注入漏洞(CVE-2024-9264)
SPIP BigUp Unauthenticated RCE(CVE-2024-8517)
CVE-2024-8517
SPIP BigUp Unauthenticated RCE(CVE-2024-8517)
Rejetto HFS 远程命令执行漏洞(CVE-2024-39943)
CVE-2024-39943
Rejetto HFS 远程命令执行漏洞(CVE-2024-39943)
Apache HugeGraph-Server Command Execution In Gremlin（CVE-2024-27348）
CVE-2024-27348
Apache HugeGraph-Server Command Execution In Gremlin（CVE-2024-27348）
Zabbix 后台延时注入(CVE-2024-22120)
CVE-2024-22120
Zabbix 后台延时注入(CVE-2024-22120)
CrushFTP 认证绕过漏洞（CVE-2024-4040）
CVE-2024-4040
CrushFTP 认证绕过漏洞（CVE-2024-4040）
No Title
CVE-2023-40267
### Summary `_clone()` validates `multi_options` as the original list, then executes `shlex.split(" ".join(multi_option
No Title
CVE-2025-54881
### Impact `@excalidraw/excalidraw@0.18.0` depends on a Mermaid conversion package version that resolves to a Mermaid r
No Title
CVE-2026-32595
## Summary There is a timing side-channel vulnerability in Traefik's BasicAuth middleware that allows an attacker to en
Alert for CVE-2026-21992
CVE-2026-21992
Alert for CVE-2026-21992
Alert for CVE-2025-61884
CVE-2025-61884
Alert for CVE-2025-61884
Alert for CVE-2025-61882
CVE-2025-61882
Alert for CVE-2025-61882
Alert for CVE-2024-21287
CVE-2024-21287
Alert for CVE-2024-21287
Alert for CVE-2022-21500
CVE-2022-21500
Alert for CVE-2022-21500
Alert for CVE-2021-44228
CVE-2021-44228
Alert for CVE-2021-44228
No Title
CVE-2025-47950
### Summary CoreDNS' DNS-over-QUIC (DoQ) server can be driven into large goroutine and memory growth by a remote client
From CRUD to Autonomous Agents: Formal Validation and Zero-Trust Security for Semantic Gateways in AI-Native Enterprise Systems
该论文针对企业软件工程从传统的确定性CRUD/REST架构向AI原生系统转型过程中引入的安全张力展开研究。在AI原生系统中，大语言模型作为认知编排器，但概率性LLMs削弱了验证、访问控制和形式化测试等经典机制的安全性。作者提出了一种由模型上
Making AI-Assisted Grant Evaluation Auditable without Exposing the Model
这篇论文针对公共机构在资助评审中引入大语言模型（LLM）作为决策辅助工具时面临的治理难题：模型和评分标准不能暴露给申请人以防他们针对优化，但评审过程必须可审计、可质疑且可问责。作者提出了一种基于可信执行环境（TEE）的架构，通过远程证明技术