Antares

Antares

Cisco
Codename MDASH

Codename MDASH

Microsoft
+
+

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About

Antares is a family of open-weight security small language models purpose-built to localize known vulnerabilities inside large codebases. Antares-350M and Antares-1B are compact enough to run locally or on premises, helping teams keep proprietary source code inside their environment while reducing inference cost and runtime. Starting from a vulnerability description, advisory, or CWE category, the model follows an iterative investigation process similar to a human analyst, it searches for relevant code patterns, reads candidate files, incorporates new evidence, changes direction when a path is unproductive, and narrows the search to the files most likely to contain the weakness. Antares returns a ranked list of potentially vulnerable source files together with the terminal exploration trace that produced the result, making findings easier to review and prioritize.

About

Codename MDASH is an agentic code scanner in Microsoft Defender that uses a multi-model AI system to detect, validate, and remediate vulnerabilities with greater depth than traditional static analysis. It extends Defender CLI with a multistage pipeline in which specialized agents collaborate across four stages. Prepare ranks files by risk using call-graph analysis and code-complexity metrics, prioritizing functions most likely to contain vulnerabilities. Scan sends ranked code to more than 100 expert agents, including injection, memory-safety, and auth-bypass auditors, with each agent focused on a specific vulnerability class. Validate combines taint analysis, type resolution through Language Server Protocol servers, and multi-model agentic debate to refine confidence and reduce false positives. Dedup consolidates overlapping results into a final set of unique actionable findings.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Security researchers and developers that need efficient open-weight AI models to locate known software vulnerabilities within codebases

Audience

Security and engineering teams that need to detect, validate, prioritize, and automatically fix vulnerabilities across software repositories with agentic AI

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Cisco
Founded: 1984
United States
blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization

Company Information

Microsoft
Founded: 1975
United States
learn.microsoft.com/en-us/security-exposure-management/ai-code-security-overview

Alternatives

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Categories

Categories

Integrations

MAI-Cyber-1-Flash

Integrations

MAI-Cyber-1-Flash
Claim Antares and update features and information
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