Code generation tools introduce a specific risk that other AI applications do not share as directly: their output is not just text a human reads, it is instructions a computer will eventually execute. A generated function that looks plausible and passes a quick glance can still contain a vulnerability, an insecure dependency, or, in more automated setups, code that runs with real permissions before anyone reviews it carefully.

Human review before merge is not optional

Generated code should go through the same review process as code written by a human, without exception carved out because "the AI probably got it right." This is not a statement about how capable current models are at producing correct code; it is a statement about the review process, which exists specifically to catch mistakes regardless of who or what produced the code. Skipping review for generated code reintroduces exactly the risk that review processes were built to catch, applied to a source that can produce subtly incorrect or insecure patterns at a volume no team can review as thoroughly if the habit of reviewing carefully erodes.

Watch for insecure patterns models reproduce from training data

Language models trained on large volumes of public code sometimes reproduce insecure patterns that were common in that training data, deprecated cryptographic functions, missing input validation, or outdated dependency versions with known vulnerabilities. This is not the model being adversarial; it is the model faithfully reproducing patterns it has seen, some of which were never good practice in the first place. Running the same static analysis, dependency scanning, and secret-scanning tools on generated code that you would run on any other code catches this category reliably.

Never let generated code introduce secrets accidentally

A model prompted with context that includes real configuration values, copied from an existing file for reference, can sometimes reproduce those values directly in generated code, effectively hardcoding a secret that was never meant to appear in source. Secret-scanning tools applied to every commit, generated or not, catch this before it reaches a shared repository, and prompts used for code generation should avoid including real secrets as context in the first place, using placeholder values instead wherever possible.

Be cautious with tools that execute generated code

Some AI coding tools go beyond suggesting code changes to actually running generated code, in a sandbox, a container, or worse, a shared development or production environment. Any execution capability deserves the same scrutiny as giving an agent a powerful tool: sandbox execution environments tightly, with no access to production credentials or data, and treat any request to execute code with broader access as requiring explicit human approval rather than automatic execution.

Prompt injection risk exists here too

If a code generation tool ingests external content, a linked issue, a pull request description, a comment in an existing file, that content is a potential indirect injection vector, the same as a document or web page in any other LLM application. An issue description crafted to include text that reads as an instruction to the model could influence what code gets generated or what actions an integrated tool takes, particularly in setups where the tool has any ability to open pull requests, comment, or modify files autonomously based on that content.

Autonomous coding agents need the tightest scope

Agents that can autonomously make changes across a codebase, open pull requests, or merge changes without human review carry substantially more risk than a tool that only suggests code for a human to accept or reject. Where autonomy is used, scope it narrowly, limit which files or directories it can touch, require human approval before any change reaches a shared branch, and maintain a clear audit trail of what the agent changed and why, since debugging an autonomous agent's mistake without that trail is considerably harder than debugging a human's.

Where TELEON fits

For autonomous coding agents with tool access to repositories, deployment systems, or execution environments, TELEON's policy enforcement, applied through the gateway or supported middleware, can require approval for higher-impact actions like merging or deploying, and its audit trail supports tracing what an agent changed and why during an investigation.

The core code-quality controls, human review before merge, static analysis, dependency and secret scanning, remain standard software engineering practices that apply regardless of how the code was generated, and they are not something a runtime policy layer replaces.

A short checklist

  1. Require human review before merge for generated code, without

exception.

  1. Run static analysis, dependency scanning, and secret scanning on

generated code the same as any other code.

  1. Avoid including real secrets as context in prompts used for code

generation.

  1. Sandbox any code execution capability tightly, away from production

credentials.

  1. Treat external content ingested by coding tools as a potential

injection vector.

  1. Scope autonomous coding agents narrowly and require approval before

changes reach a shared branch.

Code generation tools carry the same LLM security concerns as any other AI application, plus the specific consequence that their output can become executable code. Treating generated code with the same review rigor as human-written code, rather than a lighter standard, is what keeps that added risk contained.