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What is Codename MDASH?

Codename MDASH refers to a sophisticated code scanning tool that is incorporated into Microsoft Defender, utilizing a multi-modal AI framework to identify, confirm, and resolve vulnerabilities with far greater depth than traditional static analysis techniques. It enhances the Defender CLI through a multistage method where dedicated agents collaborate across four distinct phases. First, the preparation phase sorts files by risk level, applying call-graph analysis and assessing code complexity to pinpoint functions most likely to harbor vulnerabilities. Next, the scanning phase delivers this prioritized code to over 100 specialized agents, each concentrating on specific types of vulnerabilities such as injection flaws, memory safety concerns, and authentication bypasses, ensuring targeted assessments. The subsequent validation phase incorporates taint analysis and type resolution via Language Server Protocol servers, in addition to a deliberation among multi-model agents, to bolster confidence levels and reduce false positives effectively. Ultimately, the deduplication step consolidates overlapping findings into a coherent set of unique, actionable results, thereby improving the overall efficacy of vulnerability management strategies. This groundbreaking methodology marks a transformative shift in how threats are detected and remediated during the software development lifecycle. As a result, organizations can better safeguard their applications against emerging security risks.

What is Antares?

Antares is a collection of open-weight security small language models crafted to detect vulnerabilities within large codebases. Featuring models such as Antares-350M and Antares-1B, these tools can be deployed locally or on-site, ensuring that proprietary source code remains secure while also reducing both inference expenses and runtime. The procedure starts with an outline of the vulnerability, which may include an advisory or a CWE category; from there, the model embarks on a detailed investigation similar to that of a human analyst, methodically looking for relevant code patterns, scrutinizing possible files, integrating new data, and adjusting its strategy when certain paths appear unproductive. This method allows the model to concentrate its resources on the files most likely to contain the identified flaws. In the end, Antares produces a prioritized list of source files that may be vulnerable, accompanied by a comprehensive trail of the exploration process that led to these conclusions, thereby simplifying the review and prioritization for teams. Furthermore, this functionality not only accelerates the vulnerability assessment process but also significantly strengthens the overall security framework of the development environment, fostering a culture of proactive security measures. Ultimately, organizations can benefit from improved efficiency and effectiveness in managing their code vulnerabilities.

Media

Media

Integrations Supported

MAI-Cyber-1-Flash

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

learn.microsoft.com/en-us/security-exposure-management/ai-code-security-overview

Company Facts

Organization Name

Cisco

Date Founded

1984

Company Location

United States

Company Website

blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization

Categories and Features

AI Security

Not specified

Categories and Features

AI Models

Not specified

AI Security

Not specified

Open-Weight AI Models

Not specified

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