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懂王上台后可能要报复的重点人员名单明细(附下载)
CVE-2023-45290 | net-textproto up to 1.21.7/1.22.0 on Go resource consumption (Nessus ID 207754)
CVE-2024-30203 | GNU Emacs up to 29.2 Inline MIME Remote Code Execution (DLA 3801-1 / Nessus ID 207747)
CVE-2024-35548 | Mybatis Plus up to 3.5.5 sql injection
CVE-2023-31456 | Fluid Topics Platform up to 4.2 server-side request forgery
CVE-2024-38286 | Apache Tomcat up to 9.0.89/10.1.24/11.0.0-M20 TLS Handshake resource consumption (Nessus ID 208063)
CVE-2024-36485 | Zoho ManageEngine ADAudit Plus up to 8121 Technician Reports Option sql injection
CVE-2024-51990 | martinvonz jj up to 0.22.x Git Repository path traversal (GHSA-88h5-6w7m-5w56)
CVE-2024-10027 | WP Booking Calendar Plugin up to 10.6.2 on WordPress Setting cross site scripting
CVE-2024-30140 | HCL BigFix Compliance 2.0.11 Web Page Cache redirect (KB0117197)
CVE-2024-30142 | HCL BigFix Compliance 2.0.11 Session Cookie missing secure attribute (KB0117197)
CVE-2024-30141 | HCL BigFix Compliance 2.0.11 information exposure (KB0117197)
UK Regulator Urges Stronger Data Protection in AI Recruitment Tools
Cisco Desk Phone Series Vulnerability Lets Remote Attacker Access Sensitive Information
A significant vulnerability (CVE-2024-20445) has been discovered in Cisco Desk Phone 9800 Series, IP Phone 7800 and 8800 Series, and Video Phone 8875 that could allow remote, unauthenticated attackers to access sensitive information. This vulnerability, classified under CWE-200 (Exposure of Sensitive Information to an Unauthorized Actor), is due to improper storage of sensitive information within the web […]
The post Cisco Desk Phone Series Vulnerability Lets Remote Attacker Access Sensitive Information appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.
CVE-2016-1853 | Apple Mac OS X up to 10.11.4 Tcl User information disclosure (HT206567 / Nessus ID 91228)
特斯拉不再做的「科技日」,被小鹏玩明白了
Система оценки IT-навыков может интегрироваться с «Госуслугами»
Subverting LLM Coders
Really interesting research: “An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection“:
Abstract: Large Language Models (LLMs) have transformed code com-
pletion tasks, providing context-based suggestions to boost developer productivity in software engineering. As users often fine-tune these models for specific applications, poisoning and backdoor attacks can covertly alter the model outputs. To address this critical security challenge, we introduce CODEBREAKER, a pioneering LLM-assisted backdoor attack framework on code completion models. Unlike recent attacks that embed malicious payloads in detectable or irrelevant sections of the code (e.g., comments), CODEBREAKER leverages LLMs (e.g., GPT-4) for sophisticated payload transformation (without affecting functionalities), ensuring that both the poisoned data for fine-tuning and generated code can evade strong vulnerability detection. CODEBREAKER stands out with its comprehensive coverage of vulnerabilities, making it the first to provide such an extensive set for evaluation. Our extensive experimental evaluations and user studies underline the strong attack performance of CODEBREAKER across various settings, validating its superiority over existing approaches. By integrating malicious payloads directly into the source code with minimal transformation, CODEBREAKER challenges current security measures, underscoring the critical need for more robust defenses for code completion...
The post Subverting LLM Coders appeared first on Security Boulevard.