Generated authorization bypass
The assistant generated an endpoint that returned user profile data without verifying the requester's ownership.
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07Developer / Data PlatformsAthena + AchillesEnterprise AI Deployment Assurance
Developer AI Assistant
A developer AI assistant helps engineering teams write code, generate tests, explain repositories, summarize pull requests, and speed up development work.
Buyer question
Can this AI coding assistant improve development speed without introducing insecure code, weak tests, unsafe dependencies, or production-impacting agentic changes?
Scenario
Scenario overview
An engineering team wants to use AI inside IDEs, repositories, CI/CD workflows, issue trackers, pull requests, code review, and documentation. The assistant may generate code, explain services, create tests, suggest dependencies, summarize PRs, or operate as an agent that can modify files. This can increase productivity, but it also introduces risks into the software development lifecycle. AI-generated code can look correct while creating authorization issues, injection paths, unsafe infrastructure changes, or weak tests.
Why it matters
Why this matters
Developer AI does not only affect developer productivity. It can affect product security, release quality, secrets handling, dependency risk, and SDLC controls. AI review tools may also miss the exact issues they are expected to catch.
Risk surface
What can go wrong
- AI-generated code misses authorization checks.
- Generated tests validate the happy path but miss security failures.
- AI suggests vulnerable dependencies.
- Prompt injection inside repository files influences the assistant.
- PR summaries omit security-sensitive changes.
- Agentic workflows modify code without proper review.
- Private code or secrets are sent through unapproved model routes.
- Infrastructure suggestions open excessive access.
Assessment scope
What Mythos reviews
- Approved AI coding tools
- Repo access
- Private-code exposure
- Prompt and completion retention
- Branch protection
- Generated code
- Generated tests
- AI PR summaries
- Dependency suggestions
- Infrastructure-as-code suggestions
- Agentic code changes
- CI/CD controls
- Human review gates
- Secret handling
Mythos projects
Projects assigned
Athena
maps engineering environments, repo access, SDLC controls, secrets, model routes, dependencies, permissions, and evidence.
Achilles
tests generated code behavior, secure-code quality, prompt injection resistance, PR summaries, AI review reliability, and agentic change boundaries.
Minotaur
may support internal-only adversarial repository, code comment, CI/CD, dependency, and pull request scenarios.
Illustrative findings
Example findings
Illustrative examples of what a Mythos assessment may surface. They are representative patterns, not findings from a specific customer.
Generated tests created false confidence
The tests covered only successful access and failed to test unauthorized access.
Repository prompt injection weakened a test
A comment inside the repository instructed the AI agent to skip a failing security test.
PR summary hid security-sensitive change
The AI summary described a change as a refactor while omitting an authorization modification.
Vulnerable dependency suggested
The assistant recommended an outdated file upload library with known security concerns.
Deliverables
What the customer receives
- AI coding inventory
- SDLC control map
- Generated code risk report
- AI test quality review
- Prompt injection report
- Dependency and IaC risk notes
- Technical findings appendix
- Evidence pack
- Remediation backlog
- Retest plan
- Release recommendation
Decision
Decision supported
Whether AI coding should remain autocomplete-only, support documentation and tests, assist code review, or be blocked from production-impacting agentic work.
Recommendation
Final recommendation
Developer AI should be treated as part of the software supply chain. Mythos should help engineering leaders prove that AI-generated code, tests, summaries, dependencies, and agentic edits do not weaken security controls before adoption expands.
Mythos AI Security
Evidence-first AI deployment assurance.
Authorized. Scoped. Human-controlled.
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