Eval directory
Evals for Sublime Security
8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Sublime Security AI products.
About Sublime Security
Sublime Security is a programmable, AI-powered email security platform built on detection-as-code. Security teams write and tune detections in MQL (the Message Query Language) over a rich parsed message model, run them against live and historical mail for attack-surface reduction and EML analysis, and share rules through an open detection-rule ecosystem (the sublime-security/sublime-rules GitHub feed). It integrates with Microsoft 365 and Google Workspace and offers both cloud and self-hosted deployment. (Not Sublime Text the code editor.)
Employees
~100 [REQUIRES-VERIFICATION]
Industry
Email Security
Headquarters
Washington, DC [REQUIRES-VERIFICATION]
Website
sublime.securityAvailable eval packs for Sublime Security
8 packs ready to run.
Actions And Remediation
Sublime Security evals — Actions & Remediation (relift v3 InfraRed)
Attack Surface Reduction And Hunting
Sublime Security evals — Attack-Surface Reduction & Hunting (relift v3 InfraRed)
Detonation Enrichment And Ml Signals
Sublime Security evals — Detonation, Enrichment & ML Signals (relift v3 InfraRed)
Feeds And Open Rule Ecosystem
Sublime Security evals — Feeds & Open Rule Ecosystem (relift v3 InfraRed)
Message Ingestion And Eml Analysis
Sublime Security evals — Message Ingestion & EML Analysis (relift v3 InfraRed)
Mql Detection Authoring
Sublime Security evals — MQL Detection Authoring (relift v3 InfraRed)
Platform Api Auth And Deployment
Sublime Security evals — Platform, API, Auth & Deployment (relift v3 InfraRed)
Rule Lifecycle And Tuning
Sublime Security evals — Rule Lifecycle & Tuning (relift v3 InfraRed)
Why eval Sublime Security AI
Sublime Security's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for Sublime Security measures four dimensions teams care about most when deploying ai platform agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against Sublime Security's public product surface and runnable in Corsac with your own data.