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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsNot reliably. An AI agent may read the same sentence as a user instruction or as text buried in a bill PDF, email, or web page, and the words alone do not establish who has authority. A small project called taintgate takes a different approach: track where values came from, then apply allow, deny, or ask rules when the agent tries to use them in a tool call. That can put a human checkpoint in the path of a risky action—but it is a design approach, not a proven, comprehensive defense.
When does text inside a document become an instruction?
Imagine asking an agent to review a bill. The PDF contains the real account number, but also hidden or ordinary-looking text telling the agent to pay a different account. The agent can read both. As Rudratosh Shastri puts it, “An AI agent can’t tell who’s giving the orders just by reading the words.” A sentence that is harmless as a user request can be dangerous when it arrives as untrusted content the agent was asked to inspect.
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This is the prompt-injection problem in practical terms: external content can try to steer an agent into taking actions through its tools. A detector that scores suspicious wording may miss a plainly phrased instruction, while a strict detector may also flag legitimate content. The question is not only what a sentence says, but where the information used to act came from.
What taintgate checks at the tool boundary
Shastri describes taintgate as a layer between an agent and its tools. A developer wraps the tools so the gate can inspect calls and retain provenance information about values returned by tools. YAML policies can then match a tool, argument name, provenance marker, threshold, path, or host condition and choose to allow, deny, or ask for human approval.
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The author describes precedence for overlapping policies as deny over ask, and ask over allow. The approval function is supplied by the developer: it might present a confirmation button, or simply reject the call during testing. The enforcement point is the tool call, not merely the text the model reads.
Examples of policies
- Payments: ask when the recipient came from untrusted tool output, ask above a configured amount, or allow a user-specified recipient below a configured threshold.
- Email: deny forwarding to an address supplied only by untrusted email content.
- Coding agents: deny access to secret paths and block selected destructive or remote-code shell patterns.
- Web agents: deny requests to private hosts and cloud metadata addresses. The article says the implementation also recognizes an integer-form representation of the metadata IP.
- Support bots: ask for approval before refunds above a configured amount.
Why the bill example asks about both account numbers
In the payment demonstration, the gate asks for confirmation for both the malicious destination and the legitimate account number extracted from the same PDF. That is the conservative consequence of tracking origin: both values came from one untrusted document, so provenance alone cannot tell which one is the intended recipient. The real payment therefore also needs a human step.
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A commenter suggested an adjacent design: let the agent prepare or request a payment while keeping signing authority in a user-facing channel the agent cannot alter. The comment presents this idea with CAI; it is a proposal, not evidence of implementation effectiveness or user experience.
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How provenance checks differ from text detectors and human approval
| Approach | Signal and enforcement point | Disclosed trade-off |
|---|---|---|
| Text-based injection detector | Scores content wording before or while it is processed. | Can miss normal-sounding instructions; aggressive detection can block safe content. |
| Provenance-aware policy gate | Tracks the origin of values and checks them at the tool-call boundary. | Text matching can lose track of rewritten, split, or encoded values; rules must match tool argument names. |
| Out-of-band human approval | A person reviews an action in a separate user-facing authority channel. | Adds an approval step; the cited comment does not establish how well this works in practice. |
These are different control points, not a controlled head-to-head comparison. A detector evaluates content, a provenance gate evaluates values being used in a call, and an independent approval channel reserves authority for a person.
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What the reported detector results do—and do not—show
In a September 24, 2026 DEV Community article, Shastri reports testing 10 open-source detectors against 629 AgentDojo attacks embedded in ordinary-looking tool output. The article says the strongest detector caught about half while rarely blocking safe content; two detectors caught all attacks but blocked 98% of safe content. The taintgate repository, reviewed October 7, 2026, summarizes the best result more specifically as 51% detection at a 2% false-positive rate. These are author/project benchmark claims, not independent validation or evidence that taintgate itself prevents prompt injection in production.
Prompt Guard 2’s reported performance depends on the scoring setup. In the overall 629-attack test, the article says it caught six attacks. In a separate subset of 27 attacks scored one at a time, it flagged none; its highest score in that subset was 0.14. Those are distinct results with distinct denominators, and neither should be treated as a universal measure of the detector.
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Limits to account for before using the design
- Transformed values: the described provenance method relies on text matching, so rewriting, splitting, or encoding a value may prevent the gate from recognizing its origin.
- Argument names: a policy field must use the same argument name as the underlying tool; a mismatch can leave the intended check unapplied.
- Host resolution: the author says the gate does not resolve DNS names. A public-looking domain that resolves to a private address therefore needs protection at the network layer too.
- Scope of evaluation: the September 2026 article labels the package version 0.1 and says the author had not yet evaluated it in a full live-agent run. That is a publication-time statement; the cited repository may have changed since.
The detector benchmark evaluates detectors against a set of attacks. It does not establish that provenance policies catch every injection, that the demo covers every tool integration, or that the design is safe to deploy without additional controls.
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A provenance gate is most useful when an agent can take consequential actions and the system can identify which inputs supplied the action’s arguments. It gives developers a way to say, for example, “ask before paying an account number sourced from a document” or “never send a message to a recipient introduced by untrusted email.” It cannot, by provenance alone, determine whether a document-sourced account number is the legitimate one. That judgment still needs a trusted source or a person with authority to approve the action.
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