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Poland's Supreme Court Blocks Pegasus Spyware Probe
CVE-2014-6640 | DNB Trade 1.0 X.509 Certificate cryptographic issues (VU#582497)
Meow
在大湾区,探索“数据跨境”的安全密码
Галактики больше, чем мы думали? Телескопы Кека проливают свет на эволюцию звезд
CVE-2024-45327 | Fortinet FortiSOAR up to 7.0.3/7.2.2/7.3.2/7.4.3 Change Password Endpoint excessive authentication (FG-IR-24-048)
CVE-2007-2545 | Persism CMS headerfile.php system[path] Remote Code Execution (EDB-3853 / XFDB-34102)
'Ancient' MSFT Word Bug Anchors Taiwanese Drone-Maker Attacks
Siemens Industrial Edge Management Vulnerable to Authorization Bypass Attacks
Siemens ProductCERT has disclosed a critical vulnerability in its Industrial Edge Management systems. The vulnerability, identified as CVE-2024-45032, poses a significant risk by allowing unauthenticated remote attackers to impersonate other devices within the system. This flaw has been rated with a maximum CVSS score of 10.0, indicating its severe potential impact. CVE Details The vulnerability […]
The post Siemens Industrial Edge Management Vulnerable to Authorization Bypass Attacks appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.
Evaluating the Effectiveness of Reward Modeling of Generative AI Systems
New research evaluating the effectiveness of reward modeling during Reinforcement Learning from Human Feedback (RLHF): “SEAL: Systematic Error Analysis for Value ALignment.” The paper introduces quantitative metrics for evaluating the effectiveness of modeling and aligning human values:
Abstract: Reinforcement Learning from Human Feedback (RLHF) aims to align language models (LMs) with human values by training reward models (RMs) on binary preferences and using these RMs to fine-tune the base LMs. Despite its importance, the internal mechanisms of RLHF remain poorly understood. This paper introduces new metrics to evaluate the effectiveness of modeling and aligning human values, namely feature imprint, alignment resistance and alignment robustness. We categorize alignment datasets into target features (desired values) and spoiler features (undesired concepts). By regressing RM scores against these features, we quantify the extent to which RMs reward them a metric we term feature imprint. We define alignment resistance as the proportion of the preference dataset where RMs fail to match human preferences, and we assess alignment robustness by analyzing RM responses to perturbed inputs. Our experiments, utilizing open-source components like the Anthropic preference dataset and OpenAssistant RMs, reveal significant imprints of target features and a notable sensitivity to spoiler features. We observed a 26% incidence of alignment resistance in portions of the dataset where LM-labelers disagreed with human preferences. Furthermore, we find that misalignment often arises from ambiguous entries within the alignment dataset. These findings underscore the importance of scrutinizing both RMs and alignment datasets for a deeper understanding of value alignment...
The post Evaluating the Effectiveness of Reward Modeling of Generative AI Systems appeared first on Security Boulevard.
苹果向AirPods Pro 2推出7A294版固件 用于提供支持iOS 18的各种新功能
Opus Security empowers organizations to prioritize the most critical vulnerabilities
Opus Security launched its Advanced Multi-Layered Prioritization Engine, designed to revolutionize how organizations manage, prioritize and remediate security vulnerabilities. Leveraging AI-driven intelligence, deep contextual data and automated decision-making capabilities, this innovative engine helps organizations prioritize the most critical vulnerabilities, enhancing both security posture and operational efficiency. A breakthrough in vulnerability remediation Security teams are overwhelmed by the need to rapidly prioritize alerts from multiple tools across various attack surfaces. These may include redundant alerts or … More →
The post Opus Security empowers organizations to prioritize the most critical vulnerabilities appeared first on Help Net Security.