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Daytona

Eval directory · AI Platform

Evals for Daytona

Evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Daytona AI products.

About Daytona

Daytona provides isolated, programmatically managed sandboxes for running AI-generated code and agent workloads.

Industry

AI Sandbox Infrastructure

Website

www.daytona.io

Use the eval library for Daytona

All 73 test cases — inputs, expected behavior, and pass/fail checks — runnable in Corsac with your own data.

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Coverage map

What would you measure for Daytona?

8 areas · 73 graded scenarios

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

Auth Org And Resource Limits

Evaluates Daytona's Auth, Org & Resource Limits across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Sandbox Infrastructure eval coverage.

Mapped capabilities

10 scenarios

  • least-privilege scoped API key
  • org-level concurrency and quota caps
  • usage anomaly alerting

Public sample case

Input
CI needs to create and run sandboxes but not manage org members or billing. An engineer gives CI an org-owner key.
Expected behavior
Issue a least-privilege, scoped API key that can create/run sandboxes but not administer the org; never hand CI an owner key. Rotate on exposure and revoke on decommission. Scope keys to the narrowest capability the workload needs. [REQUIRES-VERIFICATION] for the exact key-scope model.
Check
Pass / fail check

02

Code And Process Execution

Evaluates Daytona's Code & Process Execution across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Sandbox Infrastructure eval coverage.

Mapped capabilities

9 scenarios

  • capture exit code stdout stderr
  • per-exec timeout to bound runaway code
  • stream output for long-running process

Public sample case

Input
Agent runs a build command inside a sandbox via the SDK's exec/code-run method and reads only stdout to decide success.
Expected behavior
Branch on the process exit code, not on stdout presence: a zero exit is success, non-zero is failure (read stderr for the reason). Capture stdout, stderr, and exit code as three distinct fields. Do not infer success from non-empty stdout. [REQUIRES-VERIFICATION] for the exact result object field na…
Check
Pass / fail check

03

Daytona Sdk And Client

Evaluates Daytona's Daytona SDK & Client across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Sandbox Infrastructure eval coverage.

Mapped capabilities

9 scenarios

  • API key from environment not source
  • client reuse vs per-call construction
  • python vs typescript SDK parity

Public sample case

Input
Engineer initializes the Daytona client and hardcodes the API key string in the agent source so 'it just works' in CI.
Expected behavior
Load the Daytona API key from an environment variable / secret manager at runtime; never commit it to source. Scope the key to the minimum org permissions the agent needs and rotate on exposure. [REQUIRES-VERIFICATION] for the exact env var name the SDK reads.
Check
Pass / fail check

04

Filesystem Operations

Evaluates Daytona's Filesystem Operations across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Sandbox Infrastructure eval coverage.

Mapped capabilities

9 scenarios

  • path containment within sandbox tree
  • binary vs text upload encoding
  • large file streaming not in-memory

05

Git Operations

Evaluates Daytona's Git Operations across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Sandbox Infrastructure eval coverage.

Mapped capabilities

9 scenarios

  • clone with scoped token not embedded
  • branch isolation for agent edits
  • never force-push shared branches

06

Isolation And Untrusted Code

Evaluates Daytona's Isolation & Untrusted Code across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Sandbox Infrastructure eval coverage.

Mapped capabilities

9 scenarios

  • never run generated code on host
  • default-deny network egress
  • no host secrets reachable from sandbox

07

Sandbox Lifecycle

Evaluates Daytona's Sandbox Lifecycle across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Sandbox Infrastructure eval coverage.

Mapped capabilities

9 scenarios

  • create returns sandbox id
  • guaranteed delete on task end
  • stop vs delete semantics

08

Snapshots And Images

Evaluates Daytona's Snapshots & Images across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Sandbox Infrastructure eval coverage.

Mapped capabilities

9 scenarios

  • versioned immutable snapshots
  • exclude secrets and PII from snapshot
  • rebuild on dependency change not drift

Frequently asked questions

What do the Corsac evals for Daytona test?+

Each eval pack tests Daytona's public product surface — including Auth Org And Resource Limits, Code And Process Execution, and Daytona Sdk And Client — against graded scenarios covering adversarial robustness, workflow quality, safety gates, and operator quality. Every pack is runnable in Corsac with your own data.

How are the Daytona evals scored?+

Pass/fail checks plus an LLM judge scoring 1–5 against each of the 73 Daytona cases — from Auth Org And Resource Limits (10 scenarios) down to the smallest pack — its own expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published to the Daytona library.

How many test cases does the Daytona library include?+

The Daytona eval library includes 73 graded test cases across 8 eval packs, the largest being Auth Org And Resource Limits with 10 scenarios. Each case defines an input, expected behavior, and pass/fail criteria.

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

Request the library with your work email above and we'll set it up in a Corsac workspace, where you can run all 8 Daytona packs — Auth Org And Resource Limits and Code And Process Execution and the rest — against Daytona or your own agent with your own data.