AI coding assistants are exposing auth and review gaps in IDEs

At a glance

What this is: This is a comparative analysis of two AI coding assistants showing that higher reasoning depth does not remove the need for identity and access review in generated authentication code.

Why it matters: It matters because IAM teams increasingly inherit code paths, auth flows, and secrets handling from AI-assisted development, which can introduce subtle identity failures across human, NHI, and autonomous workflows.

By the numbers:

• Claude Code leads adoption with over 10.1 million VS Code installs as of April 2026, compared to Gemini Code Assist's 3.7 million.
• 75% of developers use VS Code as their, their primary IDE.

👉 Read Descope's comparison of Claude Code and Gemini Code Assist for JWT auth

Context

AI coding assistants now sit on a spectrum between suggestion tools and more agentic systems that reason across a codebase before making changes. In IAM terms, that matters because the assistant can influence authentication flows, token handling, and test coverage long before anyone reviews the resulting code.

This comparison is really about governance under assistive development. When a tool can edit code, create routes, and generate tests, the security question is not whether the code compiles first, but whether the identity logic remains correct under review, deployment, and change control.

Key questions

Q: How should teams govern AI-generated authentication code?

A: Treat AI-generated authentication code as identity-sensitive change, not ordinary development output. Put it behind code-owner review, require negative-case tests, and verify token scope, field exposure, and database migration handling before merge. If the assistant touched passwords, sessions, or tokens, IAM and security approval should be mandatory.

Q: Why do AI coding assistants create IAM risk in application development?

A: They can generate plausible identity logic that still violates security boundaries, such as exposing password hashes, skipping migrations, or weakening token separation. The risk is semantic drift, where code looks correct enough to pass casual review but does not enforce the intended trust model.

Q: How do you know if assistant-generated auth tests are actually working?

A: Look for explicit coverage of failure paths, not just successful login. Good auth tests should prove that wrong passwords fail, malformed tokens are rejected, access and refresh tokens are not interchangeable, and protected endpoints do not leak sensitive fields. If those cases are absent, the test suite is incomplete.

Q: What is the difference between IDE-native assistants and terminal-native coding agents for security review?

A: IDE-native assistants usually stay closer to the editor and are easier to scope, while terminal-native agents can inspect more of the repository and make broader changes. For security review, the key difference is not branding but how much cross-file identity logic they can alter in one session.

Technical breakdown

IDE-native assistance vs terminal-native agents

IDE-native assistants stay closer to the editor and usually operate inside a narrower interaction model, while terminal-native agents can scan more of the repository and execute broader sequences of actions. That difference changes how much context they can gather before modifying files. In practice, the assistant's position in the workflow affects whether it behaves like a bounded editor helper or a code-shaping agent that can make multi-file changes with less friction. For security teams, the architectural distinction matters because broader scope increases the chance that identity logic, tests, and schema changes are edited as one coupled task.

Practical implication: classify AI coding tools by execution scope before allowing them near auth, secrets, or deployment code.

JWT auth generation and token boundary errors

JWT authentication depends on more than creating tokens and protected routes. The assistant must preserve token type separation, avoid exposing sensitive fields in responses, and keep password hashing consistent with the seeded data and database schema. In the article, one tool produced a hashed password leak and failing tests, which shows how identity code can be functionally close yet still violate security boundaries. The real risk is not syntax failure, but semantic drift between intended auth behavior and what the generated code actually enforces.

Practical implication: require review of token scope, response payloads, and seeded credentials whenever AI generates auth flows.

Test coverage as an identity control

Tests are not only a quality gate, they are an access-control proxy for generated code. Well-structured tests verify that login, token refresh, and protected routes enforce different trust boundaries, including rejection of swapped token types and malformed credentials. When an assistant skips tests or writes only happy-path checks, it leaves identity regressions invisible until runtime. In this article, the stronger result came from broader test structure, fixtures, and negative-case coverage, which is what turns auth code from plausible to reliable.

Practical implication: treat negative-path auth tests as mandatory evidence before AI-generated identity code reaches merge.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• Salesloft OAuth token breach — hackers stole OAuth tokens to access Salesforce data via Salesloft.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI coding assistants are becoming identity governance actors, not just productivity tools. Once a model can generate login flows, token validation, and route protections, it is shaping identity control outcomes inside the delivery pipeline. That makes code-assist governance part of IAM governance, especially where authentication logic is generated faster than teams can inspect it. Practitioners should treat assistant output as identity-relevant change, not generic developer convenience.

The strongest control signal is not model sophistication but whether the assistant preserves security boundaries in generated code. The article shows that one assistant could produce a working flow while still leaking hashed passwords and missing migration handling. That is a governance problem because the code looked close enough to pass casual inspection. Security teams need to evaluate whether review processes catch semantic errors, not just syntax or build failures.

Secret handling and auth correctness remain NHI problems even when the work is AI-assisted. Password hashes, JWT signing material, and API-backed login flows are still NHI-adjacent controls because they govern machine-facing trust. The assistant's role changes the speed of introduction, not the underlying identity model. IAM and PAM teams should assume AI-generated code can widen the blast radius of a bad auth pattern if review is weak.

Context-aware assistants raise the quality bar for access design, but they also raise the cost of oversight failure. The comparison suggests that broader repository reasoning can improve modularity, test structure, and auth separation, yet it also increases the surface area where a flawed decision can propagate. That means governance must expand from reviewing single snippets to reviewing cross-file identity effects. Practitioners should manage generated code as a chain of trust, not a one-off suggestion.

Claude Code and Gemini Code Assist expose a new governance question: who certifies AI-generated identity logic? The answer cannot be left to the tool or the developer alone when token boundaries, account state, and response bodies determine security. This is where IAM review, secure SDLC, and access governance meet. Teams need an explicit approval path for generated authentication code before it enters production.

From our research:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials, according to AI Agents: The New Attack Surface report.
• Only 44% of organisations have implemented policies to govern AI agents, even though 92% agree that governance is critical to enterprise security.
• That gap is why OWASP NHI Top 10 and agentic AI controls matter before assistant output reaches production auth code.

What this signals

Code-assist governance is moving into the same category as identity governance. As assistants take on more of the auth implementation burden, teams need review gates that understand token scope, password handling, and route exposure as access decisions, not just development artefacts. The practical shift is toward control points that evaluate generated identity logic before merge, not after a defect reaches runtime.

AI-generated auth code creates an identity blast radius problem. A single flawed response model or missing migration can propagate through login, refresh, and profile routes at once, so review must cover the whole trust chain. That is where a policy set aligned to OWASP Agentic AI Top 10 helps teams think about tool output, access boundaries, and downstream misuse together.

The programme-level signal is simple: if AI can draft auth code, then the organisation needs a repeatable path for validating that code against IAM standards, not just a developer's judgment. Teams that already centralise secrets, reviews, and deployment approvals will adapt faster than those treating assistants as harmless productivity add-ons.

For practitioners

• Review AI-generated auth code as identity-sensitive change Require code owners from IAM or application security to approve any generated login, token, or session logic before merge. Review response payloads, route scoping, and field exposure with the same scrutiny you would apply to manual auth code.
• Enforce negative-case tests for token boundaries Make tests for malformed credentials, token swapping, and wrong-token usage mandatory for assistant-generated authentication flows. A passing happy path is not enough when the code also needs to prove that access tokens and refresh tokens cannot be interchanged.
• Block unsafe defaults in seeded identity data Check that any sample users, password hashes, or starter databases created by an assistant are migrated, rehashed, and never exposed through profile endpoints. Treat seeded identity data as production-adjacent until proven otherwise.
• Separate assistant capability tiers by task risk Use narrower editor help for exploratory work and reserve broader codebase-aware tools for complex auth or refactoring tasks that can be fully reviewed. The deciding factor should be reviewability, not convenience.

Key takeaways

• AI coding assistants can improve speed while still producing authentication flaws that weaken identity controls.
• The article's evidence shows that test depth and security boundary handling matter more than whether the generated code compiles on the first pass.
• IAM teams should treat assistant-generated login, token, and profile code as governed change that requires formal review.

Key terms

• AI coding assistant: A software tool that generates, edits, or explains code with natural language prompts. In security terms, it becomes relevant when it can influence authentication logic, tests, secrets handling, or deployment changes that affect identity boundaries.
• Token boundary: The security line that separates one class of token from another, such as access tokens and refresh tokens. When code fails to enforce the boundary, a token meant for one purpose can be reused in a way the system was never designed to allow.
• Semantic drift: A mismatch between what code appears to do and what it actually enforces. In identity workflows, semantic drift is dangerous because the implementation can look correct in review while quietly weakening login, session, or response protections.
• Generated identity logic: Authentication or authorization code produced by an AI system rather than written manually. It still carries full security responsibility because it can create, modify, or expose access controls, session rules, and credential-handling behaviour.

Deepen your knowledge

AI-generated authentication code is a strong use case for the NHI Foundation Level course, the industry's only accredited NHI security programme. If your team is starting to govern assistant-written identity logic, it is a practical place to build that baseline.

This post draws on content published by Descope: Developer's Guide to Claude Code vs. Gemini Code Assist. Read the original.