Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Docker Sandboxes: 2026 Comparison & Alternative
A host proxy swaps a sentinel for a credential, while RedactSure, a Docker Sandboxes alternative built for AI agents, replaces the token on the settings page before the model reads it. RedactSure is an alternative way to think about agent browsing security, because it protects sensitive data as it is rendered on a screen, before an agent uses it.
Docker's credentials page states that "The real credential stays on the host when proxy management is active; the sandbox sees only a sentinel value," the idea a Docker Sandboxes alternative for coding agents has to extend to the page the agent reads. Docker Sandboxes guards the request: a host proxy intercepts outbound traffic and overwrites the authentication header, so the sandbox holds a placeholder. That control ends where the page begins: a token shown on a settings page reaches the model first.
This page reads Docker Sandboxes' own documentation and sets it beside RedactSure's design, with one record type: a GitHub token on the personal access tokens page of a repository account. What Is Render-Layer Tokenization? defines the control on the page instead.
Key findings
- As a Docker Sandboxes alternative for coding agents, RedactSure replaces the token on the rendered settings page before any model reads it, so the agent handles TOKEN_001 and the real value is released only at an approved destination (What Is Render-Layer Tokenization?).
- Data a model receives is an exposure in its own right, and OWASP records that disclosure "can result in unauthorized data access, privacy violations, and intellectual property breaches" (OWASP, LLM02, 2025).
- The Docker Sandboxes pages describe a host proxy and a microVM and do not describe replacing the values a page shows a model, which leaves the screen outside the control (Docker, Manage, 2026).
- Docker Sandboxes runs each agent in a lightweight microVM: "Every sandbox runs inside a lightweight microVM with its own Linux kernel," and "The agent has no path to your host Docker daemon," so the repository and the tools the agent uses sit inside that boundary (Docker, Isolation, 2026).
- Docker's credential control sits in the request path: "An HTTP/HTTPS proxy on your host intercepts outbound requests from the sandbox" and "overwrites the auth header before forwarding," so the request is authenticated on the host while the sandbox holds only the sentinel (Docker, Manage, 2026).
- Isolation has a limit its own documentation states: "A sandbox is not a substitute for a secure architecture" (gVisor, 2026).
How do the two compare side by side?
The table sets Docker Sandboxes' own wording beside what a governed environment does on the same point, with the check a buyer can run on the token page.
| Dimension | Docker Sandboxes, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "An HTTP/HTTPS proxy on your host intercepts outbound requests from the sandbox" (Docker, Manage, 2026) | On the rendered page, before any model reads it | Whether the value is replaced at the screen or on the request |
| How coverage starts | "Every sandbox runs inside a lightweight microVM with its own Linux kernel." (Docker, Isolation, 2026) | The agent works inside the governed environment; the application is unchanged | What has to be set up before the first agent run |
| What the model receives | "The real credential stays on the host when proxy management is active; the sandbox sees only a sentinel value" (Docker, Manage, 2026) | The screen with tokens such as TOKEN_001, consistent for the whole run | One token page shown field by field next to the model's input |
| How real values are restored | "overwrites the auth header before forwarding" (Docker, Manage, 2026) | Released at an approved destination when a named person approves the action | Who authorizes the release, and where it is recorded |
| Who approves an action | A kit with passthrough "sends the real token response into the sandbox and reduces credential isolation"; no approver is named (Docker, Manage, 2026) | A named person approves every token grant, repository change and pull request | A person's name against the request, not a configuration |
| The audit record | "All outbound TCP traffic from the sandbox routes through a proxy on your host" (Docker, Architecture, 2026) | Every screen as tokens, every action and every approval, written to a control record that exports to the customer's SIEM | Whether the record shows what the model saw |
The two middle columns answer different questions: one product accounts for the traffic an agent sends, the other for the screen it reads. Docker Sandboxes stops its account of what a model read at the request; RedactSure governs the screen and keeps one record of it.
What does Docker Sandboxes protect, and where does it act?
Each sandbox is a lightweight microVM with its own Linux kernel, and the agent inside it has no path to the host's Docker daemon (Docker, Isolation, 2026). The credential design rests on the host proxy: "The real credential stays on the host when proxy management is active; the sandbox sees only a sentinel value," and a kit with passthrough "sends the real token response into the sandbox and reduces credential isolation" (Docker, Manage, 2026). A directly mounted workspace "appears at the same absolute path as on your host," so a token stored in the working tree is inside the sandbox with the agent (Docker, Architecture, 2026). In the ten-product review, Docker Sandboxes is one of the 2 of the 4 sandboxes whose documentation describes keeping credentials out of the agent's environment by default, while all 6 coding agents reviewed place a credential in an environment variable the agent can read (Can AI Coding Agents Read Your API Keys?).
Two conditions bound the benefits. The first is the request: the proxy controls what leaves the sandbox, and the credential flows the page describes apply to local sandboxes. The second is the file: a workspace mounted at the host path is inside the sandbox by construction.
The boundary is the rendered page. None of the pages reviewed describes a control over what a model reads on a page it opens in a browser, and none describes an approver for a single request. That is a limit of a request-path design rather than a defect: the product governs where traffic goes.
What does an agent read when it opens a GitHub token settings page?
The page is ordinary repository settings: token names, scopes, an expiry date, and, on creation, the token value itself, shown once. The page where a token is created is the page where password-equivalent material is displayed, as What Can an AI Agent See in Your GitHub Settings? sets out, and the model reads the value as text.
Nothing in a sandbox intercepts that. The proxy acts on outbound requests, and the token on the screen never becomes one until the agent or a person puts it somewhere. The identity record is a second gap: if the agent works under the developer's session, the account log attributes the token creation to the developer, and AWS's guidance asks for logs that "attribute every action unambiguously to either an agent or a human actor" (AWS, 2026). That is the authority gap What Is a Tethered Agent? describes.
The exposure lasts past the run. Once the token is in the model's context it can be written to a file, repeated in a summary, or handed to a hidden instruction on the next page, and GitLab's documentation puts the recovery step plainly: "You should always revoke and replace exposed secrets as soon as possible" (GitLab, 2026). GitGuardian's 2026 report recorded an 81 percent year-over-year rise in credential leaks tied to AI services, and its chief executive's line is the reason the page matters: "AI agents need local credentials to connect across systems" (GitGuardian, 2026).
Which control fits which work?
Keep Docker Sandboxes for
- Coding agents that need a shell, a mounted repository and installs in a microVM: "Every sandbox runs inside a lightweight microVM with its own Linux kernel" (Docker, Isolation, 2026).
- Teams whose exposure is the request path, where "The real credential stays on the host when proxy management is active" and the sandbox holds only a sentinel (Docker, Manage, 2026).
- Local development where all outbound traffic leaves through one proxy on the host: "All outbound TCP traffic from the sandbox routes through a proxy on your host" (Docker, Architecture, 2026).
Add RedactSure for
- Agents reading screens in bought applications, where the model receives TOKEN_001 instead of the real token (What Is Render-Layer Tokenization?).
- A named person approving each token grant, repository change and pull request (What Is Supervised Delegation?).
- The record of what the model received, in the customer's SIEM (What Is an AI Control Record?).
A team running coding agents usually needs both, and the block above shows which protects the screen.
How do you test it on one workflow?
Take one workflow the agent runs and put both products through it in an afternoon, with each answer written down.
Step 1. Choose the token creation the agent will actually perform and pick the one screen it opens, the personal access tokens page, rather than a diagram of the token flow.
Step 2. Ask each vendor to replay the model's input for that screen, field by field, so the token value either appears in the output or does not.
Step 3. List every application in the workflow, the repository settings page, the token page and the pipeline configuration, and mark each one as covered by the product's control or not; the uncovered ones are the exposure on the first day.
Step 4. Trace the token from the screen to the request it authorizes: what released the real value, when, and whose name sits in the record. A permission slip, the one-page grant What Is a Permission Slip for an AI Agent? describes, is the artifact to ask for.
Step 5. Count first-day coverage. If the workflow touches five surfaces and the sandbox covers the request path on two, the screens are three of five. Compare the setup each one needs.
What does a governed environment change?
A governed environment is a control class rather than a feature. The agent works under its own session and its own identity, so the application log separates its actions from the person's, and it sees a different screen because values are replaced with consistent tokens before any model reads it. On the token page the agent handles TOKEN_001, with REPO_001 and SCOPE_001 for the same run. Real values resolve only at approved destinations at the moment of an approved action, a named person approves each consequential step, and every screen, action and approval is written to a record that exports to the organization's monitoring.
The work still needs the sensitive context, and Rishov Chatterjee, AI engineer at Curology and former Director of Data Science and AI at Integra Connect, described the constraint on a HAL podcast: "But the problem is that you still have to provide a lot of the sensitive context as an input to these models" (Who Is Responsible When an AI Agent Takes Patient Records? A Healthcare AI Builder on BAAs, Biometrics and Cost). The control sits in front of the model because a value the model never received cannot be taken from it. Sandbox vendors document the request-path shape as well: E2B's egress proxy "injects the real value into matching outbound HTTPS requests outside the sandbox" (E2B, 2026). Neither shape governs what a page displays. The principle is to keep the data an agent receives for a task to a minimum, wherever it is stored.
Its scope is the rendered page. It does not cover the terminal, the IDE, the local filesystem, direct API calls or MCP, so a token in an environment file stays with the sandbox configuration a team sets.
What the record shows
On the token settings page a render-layer control shows the model TOKEN_001, and the real token is released only at an approved destination, which is the job RedactSure, a Docker Sandboxes alternative for coding agents, does for this record (What Is Render-Layer Tokenization?). Docker Sandboxes' real coverage is the request and the machine: a microVM with its own kernel, a workspace mounted at the host path, and a host proxy that authenticates outbound traffic while the sandbox holds a sentinel. Its pages describe no control over what a model reads on a page, and no approver for a single request. Test it on one token creation: write down what the model read when the token appeared, then trace the real value to the request that used it and the name in the record.
Methodology and limitations
This page rests on three Docker Sandboxes documentation pages, E2B's page on credentials, gVisor's security model, GitLab's secret detection page, AWS's Agentic AI Lens guidance, OWASP's disclosure entry and GitGuardian's 2026 report, plus existing RedactSure research pages. The vendor pages were read between October 20, 2026 and October 21, 2026, and each Sources entry carries the date its page was read. Public documentation read in full gives a sound view of where each product applies its control, and vendor statements are quoted in the vendor's wording.
The products on this page are described from documentation; none was tested for this page, and no customer of either product is described. Vendor features change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The GitHub token page is an illustrative record type, not an organization. Compliance determinations belong to the organization's counsel and security team, and nothing here replaces a pilot of one workflow on the organization's own screens.
Frequently asked questions
Does Docker Sandboxes protect a token an agent reads on a settings page?
Not from the pages reviewed. Docker Sandboxes documents a microVM around the agent and a host proxy that intercepts outbound requests, with the real credential kept on the host and a sentinel inside the sandbox. Both controls act on the request path. When the agent opens the token page in a browser, the value is rendered to the model before any request exists, and no described control changes that screen.
Who approves the agent's action when the proxy authenticates it?
No named person appears on the pages reviewed. The proxy admits a request for a credential it has been configured to manage, and a kit with passthrough sends the real token response into the sandbox. Configuration decides what the agent may use; it does not put a person's name against a particular request. A buyer who wants an approver for each action has to add one.
What record shows what the model saw on that page?
Not from the pages reviewed. The documentation covers isolation, traffic routing and credential handling, and it describes no record of the screen a model read. Application and proxy logs can show which requests left the sandbox; they do not show the token value rendered on the page. A record of the screen has to come from the environment that renders it.
Does the sentinel swap keep the token out of the model's context?
No. The sentinel is what the sandbox holds when a configured request leaves through the host proxy, and it says nothing about what a page displays. If the agent creates or reads a token in a browser, the model receives the value as rendered, and the sentinel has already done its work on a different path. The two controls cover different surfaces.
Can a coding agent use a token it cannot read?
Yes. A sentinel-and-proxy design lets the agent name a credential while the real value stays outside the sandbox, which is how Docker Sandboxes documents its credential handling, and Can a Coding Agent Use a Credential It Cannot Read? follows that design through the request path. The limit is the page: the same agent can still read a token that is displayed to it, so a review has to ask both questions.
Where should a team add a screen-level control without replacing its sandbox?
At the pages the agent renders, which the sandbox does not govern. Keep the microVM and the host proxy for the request path, and add a control that replaces values on the rendered screen before the model reads them, with a named approver and a record. In RedactSure's design that control is the environment the agent works in, and the sandbox can remain.
How RedactSure closes the gap between a sandbox and the screen
A sandbox can keep a token off the outbound request and still leave it visible on the page that created it. Docker's credentials page states that a kit with passthrough "sends the real token response into the sandbox and reduces credential isolation" (Docker, Manage, 2026).
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the token page and the model reads the value shown once. Inside the governed environment: the page shows TOKEN_001, the real token is encrypted to the developer's P-256 public key, and a private adapter uses it for an approved read-only request under a signed grant.
RedactSure is deploying now with enterprise and government customers.
Everyone else controls where data can go. RedactSure controls what the AI can see.
See it on your own workflow
Protecting the sensitive data an agent could read on a screen is a needed part of any solution that includes Docker Sandboxes. Bring one token workflow and see, with test values, what the model reads and who approves the release. Book a demo