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Description
Antares represents a suite of open-weight security small language models specifically designed to identify existing vulnerabilities within extensive codebases. With models like Antares-350M and Antares-1B, organizations can operate them locally or on-site, allowing for the protection of proprietary source code while also minimizing both inference costs and runtime. The process begins with a description of the vulnerability, an advisory, or a CWE category, where the model engages in a step-by-step investigation akin to that of a human analyst, systematically searching for pertinent code patterns, examining potential files, assimilating new information, and altering its approach when certain avenues prove unfruitful. This strategy enables the model to focus its efforts on the files that are most likely to harbor the identified weaknesses. Ultimately, Antares generates a prioritized list of potentially vulnerable source files along with the detailed exploration trail that led to these findings, facilitating easier review and prioritization for teams. Moreover, this capability not only streamlines the vulnerability assessment process but also enhances the overall security posture of the development environment.
Description
Codename MDASH represents an advanced code scanning tool integrated within Microsoft Defender, leveraging a multi-modal AI framework to uncover, verify, and address vulnerabilities with a level of insight that surpasses conventional static analysis methods. This system enhances the Defender CLI by introducing a multistage process where specialized agents work collaboratively through four distinct phases. Initially, the preparation stage organizes ranked files based on risk, utilizing call-graph analysis and evaluating code complexity to identify functions with the highest likelihood of containing vulnerabilities. The scanning phase then transmits this prioritized code to over 100 specialized agents, including those focused on injection flaws, memory safety issues, and authentication bypasses, ensuring each agent targets a specific category of vulnerability. Following this, the validation phase employs taint analysis, type resolution through Language Server Protocol servers, and a debate among multi-model agents to improve confidence levels and minimize false positives. Finally, the deduplication step merges overlapping findings into a comprehensive set of unique and actionable results, enhancing the overall efficiency of vulnerability management processes. This innovative approach signifies a new era in threat detection and remediation within software development.
API Access
Has API
API Access
Has API
Integrations
MAI-Cyber-1-Flash
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Cisco
Founded
1984
Country
United States
Website
blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
learn.microsoft.com/en-us/security-exposure-management/ai-code-security-overview