Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2023-0676PUBLISHED: 2023-02-04Cross-site Scripting (XSS) - Reflected in GitHub repository phpipam/phpipam prior to 1.5.1.
CVE-2023-0677PUBLISHED: 2023-02-04Cross-site Scripting (XSS) - Reflected in GitHub repository phpipam/phpipam prior to v1.5.1.
CVE-2023-0678PUBLISHED: 2023-02-04Improper Authorization in GitHub repository phpipam/phpipam prior to v1.5.1.
CVE-2023-0673PUBLISHED: 2023-02-04
A vulnerability classified as critical was found in SourceCodester Online Eyewear Shop 1.0. Affected by this vulnerability is an unknown functionality of the file oews/products/view_product.php. The manipulation of the argument id leads to sql injection. The attack can be launched remotely. The asso...
CVE-2023-0674PUBLISHED: 2023-02-04
A vulnerability, which was classified as problematic, has been found in XXL-JOB 2.3.1. Affected by this issue is some unknown functionality of the file /user/updatePwd of the component New Password Handler. The manipulation leads to cross-site request forgery. The attack may be launched remotely. Th...
User Rank: Apprentice
1/12/2018 | 4:37:14 PM
There are great places where ML is perfect for cybersecurity, but only as a part of the solution. The heart of our Constellation Analytics Platform is a Bayesian Inference Network built over years of research to reason like a team of cyber experts across weak inputs from disparate sensor systems. Some of those sensors need ML techniques to find the real signal, some don't - but they all come together in a BayesNet that prioritizes the threat and ultimately the risk.
We believe this is the only viable approach today given what you highlighted of lack of training data, and even if you had some training data, sensor outputs change frequently as the environment changes. Only with additional context can you uncover those malicious events that represent the highest risks.