AntaresCisco
|
Codename MDASHMicrosoft
|
|||||
Related Products
|
||||||
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
University cybersecurity labs that need efficient, locally deployable models for locating vulnerabilities in sensitive codebases
|
Audience
DevSecOps teams managing large polyglot repositories that need deeper vulnerability detection and AI-assisted remediation inside existing delivery pipelines
|
|||||
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/
|
Reviews/
|
|||||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||||
Company InformationCisco
Founded: 1984
United States
blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization
|
Company InformationMicrosoft
Founded: 1975
United States
learn.microsoft.com/en-us/security-exposure-management/ai-code-security-overview
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
|
|||||
|
|
|
|||||
|
|
||||||
Categories |
Categories |
|||||
Integrations
MAI-Cyber-1-Flash
|
||||||
|
|
|