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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchRunning an AI coding agent on-premises does not, by itself, keep source code or credentials safe. Security comes from limiting what the agent can read, which tools and identities it can use, where it can connect, and which actions require independent approval. Treat the agent as an untrusted actor with narrowly scoped access—not as a trusted developer simply because it runs inside your network.
Map the trust boundaries before enabling the agent
Draw the developer, agent process, model endpoint, repository, CI runner, tool or MCP servers, and internal network as separate zones. For each connection, record what data or authority crosses it: source files sent to inference, credentials passed to tools, commands run by the agent, and results written back to source control.
On-premises describes where some part of the system runs; it does not establish that inference, telemetry, or all processing stays inside the organization. Depending on the architecture, code may be sent to a model endpoint outside the local runtime. Check the actual product documentation and configuration for data flow and retention rather than assuming a location guarantee.
OWASP’s Secure Coding with AI Cheat Sheet identifies repository content, model providers, MCP servers, and CI/CD as relevant trust boundaries. Treat repository files, issues, pull requests, web content, error traces, and tool descriptions as untrusted input. Any of them can contain instructions intended to manipulate the agent. Local hosting does not solve prompt injection; constrain the authority available to the agent even if it follows malicious input.
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How do I apply least privilege to an AI agent?
Give the agent a dedicated identity instead of a developer’s personal account. Scope that identity to the repository or project needed for the task, start with read-only access where possible, and grant narrowly bounded write access only when the workflow requires it. Enforce permissions in source control and the execution environment, not through a prompt asking the model to behave safely.
Separate permission to inspect or propose a patch from permission to merge it, change branch protections, modify CI/CD workflows, access organization secrets, or deploy. For each permission, document the resource, allowed action, duration, owner, and approval path. A task that needs to edit application code should not automatically inherit permission to change the systems that build or release that code.
| Task | Starting access | Additional authority to keep separate |
|---|---|---|
| Explain code or investigate a bug | Read access to the relevant repository and issue or trace | Writing files, pushing changes, accessing secrets |
| Prepare a code change | Read access plus write access limited to a working branch or patch workflow | Merging, changing branch protections, editing release workflows |
| Build or test code | Access to the necessary build inputs and isolated execution tools | Production credentials, unrelated repositories, unrestricted internal network access |
| Release or deploy | Do not inherit this authority from coding access | Require a distinct, explicitly authorized release process |
This is a practical separation of duties, not a claim that every source-control platform exposes these exact permission bundles. Map the boundaries to the controls your platform actually provides. OWASP’s AI Agent Security Cheat Sheet and NIST NCCoE’s February 2026 concept paper, Accelerating the Adoption of Software and AI Agent Identity and Authorization, discuss agent identity and authorization as design concerns.
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How should I sandbox an AI coding agent?
Run agents that execute shell commands or install packages in a restricted shell, sandboxed container, virtual machine, or disposable workspace. The isolation should cover the agent’s whole execution context—not just its process. Inspect what directories are mounted, which caches persist, what credentials are available, and what internal services are reachable.
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- Keep SSH keys, cloud CLI configuration, developer credential stores, and production data out of the runtime unless a documented task requires them.
- Use command or tool allowlists where practical, and review MCP servers before enabling them. Tool descriptions can carry untrusted instructions, and tool behavior or definitions can change.
- Restrict outbound network access to required destinations; do not give the agent general access to internal services by default.
- Apply appropriate limits to CPU, memory, processes, storage, and execution time, and clean up the workspace and caches after the task.
A container or VM is a containment measure, not proof of isolation. Validate the effective mounts, identity, network routes, and cleanup behavior in the specific deployment.
Keep credentials out of the agent context
Do not place deployment keys, production credentials, broad personal tokens, or organization-wide secrets in an agent environment when the task does not need them. Prefer short-lived credentials scoped to the task and the smallest necessary resource and action. OWASP’s coding-agent guidance specifically recommends task-scoped ephemeral credentials.
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If a task genuinely requires a credential, deliver it through a controlled mechanism and limit its lifetime. Check that prompts, command arguments, tool results, model context, and logs do not expose the value. A secrets-management service can help control delivery and scope, but using one does not by itself prevent an agent from reading, misusing, or disclosing a credential it is authorized to access.
Require independent approval for high-impact actions
Require human authorization before operations such as changing access policy, editing CI/CD definitions, pushing to protected branches, deploying, or accessing sensitive data. Make the approval specific to the action: identify the actor, tool, target, normalized parameters, time, and expiry. The component that executes the operation should validate the authorization independently and deny the action if authorization or audit checks fail.
A general approval prompt is weaker than an authorization check bound to the actual operation. A change to one file or target should not silently authorize a different command, destination, or set of parameters. Keep merge, policy-change, and deployment authority outside the agent’s ordinary code-editing permission.
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Can a self-hosted runner expose secrets?
Yes. A self-hosted runner may have cached credentials or access to internal services, and untrusted workflow code can compromise a persistent runner. “Self-hosted” describes who operates the runner; it does not guarantee a clean, isolated, or disposable environment.
OWASP’s GitHub Actions Security Cheat Sheet and GitHub’s Secure use reference warn about runner and workflow-token risks. GitHub specifically cautions that self-hosted runners are not guaranteed to run in clean ephemeral VMs and that untrusted workflow code can persistently compromise a runner.
- Separate runner groups by privilege and network reachability; keep ordinary linting and analysis away from runners with more sensitive build or release access.
- Restrict which repositories and workflows can target each runner group.
- Do not make secrets available to untrusted jobs, and review external contributions before allowing them to run with sensitive access.
- Use ephemeral runner environments for untrusted work where possible, then destroy them after the job.
- Review workflow permissions and tokens as carefully as runner credentials; limit both to the actions the job requires.
Monitor activity and test the controls
Keep audit records of tool calls and authorization decisions with enough context to reconstruct activity, while excluding credentials and avoiding unnecessary copies of sensitive source code in ordinary logs. Alert on unexpected file changes, network calls, secret access, privilege changes, and signs that a runner or workspace persisted beyond its intended lifetime.
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Test the controls with realistic attempts to:
- Inject instructions through repository documents, issues, or pull requests.
- Use a tool outside the task’s intended scope or access a credential the agent should not need.
- Bypass approval or alter the approved target or parameters before execution.
- Reach disallowed network destinations or leave files, processes, or credentials behind after cleanup.
GitHub documents secret scanning through its remote MCP server as an example of a supplemental check. Its findings are ephemeral to the current agent session; they do not become Security-tab alerts or API findings, and the feature does not support local MCP server configurations. Do not treat that session-only result as durable detection or as a substitute for your organization’s normal secret-scanning and incident records.
Evaluate the actual deployment, not the “on-premises” label
For each candidate architecture, verify the controls below in product documentation and configuration. The reviewed OWASP, NIST, and GitHub materials establish why these dimensions matter, but they do not rank on-premises products or establish data-flow guarantees for every vendor.
- Repository scope: Can access be restricted to a required repository or project, and can read and write permissions be separated?
- Execution isolation: What OS-level sandbox is used, and what files, credentials, processes, and internal services can it reach?
- Credential handling: Are credentials task-scoped and short-lived, and are they kept out of prompts, logs, and unrelated tools?
- Network and tools: Can outbound traffic and internal reachability be restricted? Are MCP servers and tool-definition changes controlled?
- Approvals and source control: Can sensitive operations be gated independently, and do branch protections remain outside the agent’s authority?
- Runner lifecycle: Are workspaces and runners ephemeral, and is cleanup verifiable?
- Auditability: Which tool calls, approvals, network events, and secret-access events are recorded, and how long are those records retained?
- Data flow: Does inference or telemetry leave the organization’s boundary, and what do the applicable vendor documentation and configuration say about retention?
What GitHub’s Copilot cloud-agent controls do—and do not—show
GitHub’s documentation for Copilot cloud agent says it responds only to users with repository write access, is constrained to the repository where it creates a pull request, cannot push directly to the default branch, and lacks access to Actions organization or repository secrets except secrets specifically configured for the Copilot environment. These are documented controls for GitHub’s cloud agent. They are not evidence that a separately deployed or on-premises coding agent has equivalent boundaries; verify the controls in the system you operate.
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