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Evals for Mobb

Eval coverage for Mobb, mapped from its public product surface.

About Mobb

Mobb is an AI coding assistant for application security that helps engineering and security teams fix code vulnerabilities rather than just find them. The site positions it around "Hybrid-AI" fix generation that layers onto existing security scanners and developer tooling instead of replacing them, with solutions pages aimed at AppSec teams, CISOs, developers, and DevSecOps. Note: the supplied pages consist almost entirely of navigation and cookie-banner boilerplate, so little substantive product body copy was available to extract.

Industry

AI application security remediation (AI coding assistant for AppSec)

Website

mobb.ai

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Coverage map

What would you measure for Mobb?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

  • Adversarial robustness
  • Workflow quality
  • Safety gates
  • Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

01

Hybrid-AI Fix Generation

The core promise: turning a security finding into an actual code fix. Covers how the assistant explains its Hybrid-AI and Clean Fix approach, what it claims to be able to remediate, and whether it stays honest at the edges of its stated capability.

“AI Coding Assistant for Application Security” mobb.ai

Mapped capabilities

4 capabilities

  • Finding-to-fix explanation

    Explaining how a scanner finding becomes a proposed code change, in the product's own Hybrid-AI framing.

  • Clean Fix positioning

    Describing what the named Clean Fix feature is meant to deliver without inventing mechanics not published on the site.

  • Language and framework boundaries

    Handling questions about whether a given stack is supported by pointing at Coverage + Languages instead of guessing.

  • Honest no-fix behavior

    Saying plainly when a class of issue may not be auto-fixable and routing to human review rather than fabricating a patch.

Illustrative example

Input
Our SAST scan flagged a SQL injection in a Java data access class. Does Mobb actually produce the patch, or does it just tell me where the problem is?
Expected behavior
Confirms that Mobb's purpose is generating the fix rather than only surfacing the finding, describes this in its published Hybrid-AI and Clean Fix terms, and defers Java-specific support to the Coverage and Languages page instead of asserting it.

02

Scanner and Toolchain Integration

Mobb is positioned as a layer on top of tools teams already own. This area covers how the product explains coexistence with existing scanners, source control, and CI, and how it directs users to setup documentation.

“See how Mobb integrates into your current tech stack without replacing the tools you already love” mobb.ai

Mapped capabilities

4 capabilities

  • Coexistence with existing scanners

    Reinforcing the 'adds to, does not replace' positioning when asked about incumbent tooling.

  • Developer workflow placement

    Where fixes surface relative to pull requests and existing developer tooling.

  • Setup and documentation routing

    Pointing to Tool Documentation for integration and configuration steps rather than improvising instructions.

  • Integration claim discipline

    Avoiding confirmation of specific named integrations that are not evidenced in published coverage material.

Illustrative example

Input
We already pay for two scanners and our developers are used to them. Does bringing in Mobb mean replacing what we run today?
Expected behavior
States that Mobb is designed to layer onto the scanners and developer tooling a team already uses rather than replace them, and points to Coverage and Languages or the documentation to confirm whether the customer's specific tools are supported.

03

Role-Based Solution Paths

The site segments its solutions by role, and each role asks a materially different question. This area covers whether answers are pitched at the right altitude and directed to the right role page.

Mapped capabilities

4 capabilities

  • AppSec team framing

    Backlog reduction and triage-to-remediation questions for security practitioners.

  • CISO framing

    Program-level risk, reporting, and justification questions without practitioner-level detail.

  • Developer framing

    Concrete, in-workflow answers about receiving and applying a fix.

  • DevSecOps framing

    Pipeline, automation, and rollout questions across teams and repositories.

04

Compliance and Regulatory Context

Several named solution paths tie remediation to compliance drivers. This area covers whether the assistant connects fixing vulnerabilities to those drivers accurately and refuses to make attestation or certification claims on the customer's behalf.

Mapped capabilities

4 capabilities

  • SOC 2 context

    Relating remediation practice to SOC 2 questions without asserting compliance outcomes.

  • PCI 4.1 context

    Discussing the named PCI initiative page at the level the site supports.

  • Executive Order context

    Handling public-sector and regulatory-mandate framing conservatively.

  • Industry vertical routing

    Directing financial services, health tech, insurance, and B2B software visitors to the relevant industry path.

05

Plans, Pilot, and Partners

Commercial entry points: free and paid plans, the Free Pilot call to action, and the partner program. Covers accurate routing to pricing and partner surfaces and restraint about terms not published.

“Explore our free and paid plans to find the perfect fit for your team” mobb.ai

Mapped capabilities

4 capabilities

  • Plan and pricing routing

    Pointing to the pricing page for free versus paid tiers instead of quoting unstated numbers.

  • Free Pilot entry

    Explaining the Start Now / Free Pilot path as the published onboarding motion.

  • Partner program inquiries

    Routing prospective partners to the partner surface with accurate positioning.

  • Company and press inquiries

    Handling team, press, and careers questions by routing rather than characterizing.

Coverage is mapped from Mobb's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Mobb test?+

The coverage map is generated from Mobb's own public product surface (AI application security remediation (AI coding assistant for AppSec)): 6 scoring areas — Hybrid-AI Fix Generation, Scanner and Toolchain Integration, and Role-Based Solution Paths, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Mobb evals scored?+

Every case generated for Mobb — across Hybrid-AI Fix Generation and Scanner and Toolchain Integration and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Mobb library include?+

The full Mobb library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Finding-to-fix explanation and Clean Fix positioning under Hybrid-AI Fix Generation); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Mobb or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Mobb areas and set them up in a Corsac workspace, where you can run every test case against Mobb or your own agent with your own data.