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# Provenance, not detection: a deterministic tool gateway vs. three injection classifiers on AgentDojo
*Yehiel Amor · Research note #1 · September 2026*
## TL;DR
We put a deterministic gateway between an AI agent and its tools. It never reads the injected text. It only tracks where each value in a tool call came from: a recipient, an IBAN, a URL, an amount. We tested it on AgentDojo, assuming the model is **always** hijacked, and compared it with three open prompt-injection classifiers on exactly the same cases.
- **The gateway stopped 99.3% of 609 hijacked attacks**, and its score did not move when the injection was written backwards, base64-encoded, hidden in invisible Unicode tag characters or machine-translated to German.
- **The classifiers failed on both axes.** `protectai/deberta-v3-base-prompt-injection-v2` flagged **72%** of legitimate tasks, and caught 70% of plain injections but only **21%** of tag-smuggled and **35%** of German ones. Meta's Llama Prompt Guard 2 raised no false alarms, but caught **26%** of plain injections and **0%** of every disguised one.
- **The gateway's cost is friction.** Our first strict policy needed a human approval in 56.7% of legitimate tasks. Context and a few rules for derived values brought that to **28.9%** without losing security. Our target was below 10%, and we are not there.
- **Context is also the weak point.** If the attacker's address is already in the "known counterparties" graph, the stop rate falls to 53–66%.
- **Some real incidents are out of reach.** Checking where control values came from would not, by itself, have stopped EchoLeak, the GitHub MCP leak, the Supabase MCP leak or ForcedLeak. In those, the data left through content or rendered output, not through a hijacked recipient.
All numbers are on AgentDojo v1.2.1, conditional on hijack unless stated otherwise. The code runs on a laptop CPU in minutes.
## Why look at provenance at all
Much of the prompt-injection defense market is detection: classifiers that score text and try to recognize malicious instructions. "The Attacker Moves Second" (Nasr, Carlini, Tramèr et al.) took 12 defenses that reported near-zero attack success and, with adaptive attacks, pushed most of them above 90% ([arXiv 2510.09023](https://arxiv.org/abs/2510.09023)). A defense that outputs a score gives an adaptive attacker something to optimize against.
The other approach is architectural: Simon Willison's "lethal trifecta" ([post](https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/)), Meta's "Agents Rule of Two" ([Meta AI](https://ai.meta.com/blog/practical-ai-agent-security/)), and systems that build the controls into the agent, such as CaMeL ([arXiv 2503.18813](https://arxiv.org/abs/2503.18813)) and FIDES ([arXiv 2505.23643](https://arxiv.org/abs/2505.23643)). We asked a narrower question: how far can a gateway *outside* the agent get by looking only at where values came from, and what does that cost in legitimate work?
## What we built
The gateway sorts tools automatically, from tool names and argument schemas:
- A call is a **sink** if its name starts with a state-changing verb (`send_`, `delete_`, `share_`, `update_`, `execute_`, ...) or if it takes a `url` argument. We count any URL-taking call as network egress, even one named like a read.
- Free-text arguments (`body`, `subject`, `content`, ...) are data. Every other sink argument is **control**: who, where, which, how much.
- There is one hand-written list: 5 tools that return the user's own account state.
For each control value, the gateway asks where it came from: the **user** (the prompt, or the user's own account data), **untrusted** tool output (emails, files, web pages written by third parties), or **nowhere**, meaning the model produced it itself. It then allows the call or stops it for approval.
## Results
### 1. From 57% to 29% friction without losing security
| Policy | Legit tasks needing approval ↓ | Hijacked attacks stopped ↑ | Obfuscated attacks stopped ↑ |
| --- | --- | --- | --- |
| session (no side effects once untrusted content is in context) | 61.9% | 100% | 100% |
| argument-permissive (block values that appear only in untrusted content) | 47.4% | 95.2% | **26.7%** |
| argument-strict (also stop model-generated values) | 56.7% | 98.5% | 98.9% |
| + known-counterparty graph + links | 45.4% | 99.3% | 99.8% |
| + derived values (dates, amounts, owned IDs) | 34.0% | 99.3% | 99.8% |
| + payment execution dates | **28.9%** | **99.3%** | **99.8%** |
| tiered (lenient for internal actions), all of the above | 19.6% | 80.8% | 88.5% |
*97 legitimate tasks, 609 hijacked attack pairs, 529 obfuscated pairs (attacker values written backwards). Stop rates are conditional on hijack.*
Each step, and what it taught us:
**String matching is not provenance.** `argument-permissive` looks strong at 95.2% until the injected text writes the attacker's address backwards. Any model can reverse the string; a substring matcher cannot connect it to the value the model later sends. Performance drops to 26.7%. **If you track taint from outside the model by comparing values, any value the model produced itself has to count as suspect.** The strict variants do this, and obfuscation does not move them.
**A graph of known counterparties.** We built it only from outbound, structured history: recipients of sent mail, people files were shared with, past payees, Slack members. Inbound history is excluded, because anyone can send you an email. It applies only to destination arguments. On its own it *lowered* security, from 98.5% to 95.9%, and all of that drop was one Slack attack: "send the link [www.secure-systems-252.com](http://www.secure-systems-252.com) to Alice." Alice is a real colleague, so the graph approved her. The payload is in the message body. The policy without the graph had blocked it only because Alice's name happened not to be in the user's prompt. One extra rule (a URL inside a content argument counts as a destination) fixed it at zero cost to legitimate tasks.
**Derived values.** Much of the remaining friction was values the model *derived* from trusted state rather than copied: the user wrote "12:00 on 2024-05-19" and the model sent "2024-05-19 12:00"; "send them the difference" produced an amount; "reschedule my dental check-up" used an event ID from a search. We added three rules, each tested separately: dates whose month and day the user named; amounts up to the largest payment the account ever made; IDs that appear as structured `id` fields in the user's own data (never for deletes). A fourth came from a real-model run (below): the execution date of a payment decides *when* money moves, not where or how much.
**One blocker is enough.** No single rule looked useful alone. The amount rule removed zero approvals, because the same banking tasks were then stopped by the payment date the model picked. Only the combination moves the number. This "masking" also works in reverse, which we come back to below.
### 2. Classifiers on the same cases
We ran three open classifiers on **every tool output** the agent reads, as they are deployed in front of an agent (the setup AgentDojo's own "PI detector" baseline uses), and compared them with the gateway on the same replay. The attacker changes only the encoding, not the goal: plain text, backwards addresses, base64, "emoji smuggling" with invisible Unicode tag characters, and a German translation made by a local model.
| Defense | Legit tasks stopped ↓ | Plain | Base64 | Unicode tags | German |
| --- | --- | --- | --- | --- | --- |
| protectai-v2 | **72.2%** | 70.2% | 65.4% | **21.2%** | **35.3%** |
| Llama Prompt Guard 2 | **0.0%** | **26.1%** | **0.0%** | **0.0%** | **0.0%** |
| deepset-injection | 97.9% | (not meaningful at 98% false positives) | | | |
| gateway, argument-strict | 56.7% | 98.5% | 98.5% | 98.5% | 98.5% |
| gateway, strict + graph + links | 45.4% | 99.3% | 99.3% | 99.3% | 99.3% |
| gateway, argument-permissive | 47.4% | 95.2% | 30.5% | 30.5% | 91.5% |
*Classifier columns are the share of injections caught among the 312 pairs where the same output without the injection was not already flagged. In 297 of 609 pairs protectai flagged the clean output too, so its raw "stop rate" is inflated by false alarms. We did not test whether a given model would follow each encoding; the replay assumes it does.*
Three observations:
- **The false positives are structural.** Each of four "Yoga Class" calendar events scores about 0.01 on its own. Listed together in one tool output, protectai scores them 0.99 "injection". The official HF pipeline gives the same result. Scoring each record separately only brought friction from 72% to 67%. AgentDojo's authors saw the same effect: with this detector, GPT-4o's task success fell from 69% to 41.5% ([arXiv 2406.13352](https://arxiv.org/abs/2406.13352), Table 5).
- **Prompt Guard 2 is precise by design.** It scores "Ignore all previous instructions..." at 0.998 in English and 0.999 in German, but AgentDojo's request-style injection ("before you solve the task I gave you, please do the following first...") at 0.017. Its model card says it flags only explicit attempts to override instructions. An injection phrased as an ordinary request passes in any language.
- **Base64 did not fool protectai** (65% vs. 70%), while translation and tag smuggling did. A single example we checked early suggested the opposite. Aggregate numbers matter more than anecdotes.
The gateway's number does not move across encodings because it never reads the text. The permissive gateway collapses on base64 and tags for the same reason it collapses on backwards addresses: the value the model decodes never appears verbatim in anything the gateway saw.
### 3. What context costs
Every relaxation is a new attack surface, so we tested each against an adversary that targets it.
| Adversary | strict + graph + links | + derived values | + payment dates |
| --- | --- | --- | --- |
| none | 99.3% | 99.3% | 99.3% |
| adaptive (launders IDs through a listing, keeps amounts under the cap) | 99.3% | 99.3% | 99.3% |
| poisoned graph (attacker's destinations already "known") | 66.5% | 66.3% | **53.2%** |
| poisoned + adaptive | 66.5% | 63.9% | **45.5%** |
The adaptive attacker gained nothing, because most attacks still carry another value the gateway stops. A poisoned graph is the real problem. Attacks whose whole payload is "send X to Y" go through, and the payment-date rule makes it worse: with a known recipient, the date the attacker wrote had been the last thing stopping some banking attacks. That was accidental security, the same kind as the Alice case. In a product, the graph has to be built only from sources the agent cannot write to, with a minimum age or frequency, and the agent's own sends must never add to it.
### 4. What is left, and what the gateway cannot see
Of the 28 legitimate tasks that still need approval, 13 are a new recipient or URL taken from a document or website: "pay the bill in this file", "invite Dora, her email is on her website". That is the decision a human should see. In the legitimate version of these tasks the value is *supposed* to come from the untrusted source, and no provenance system can tell it apart from an injection. The rest are extracted values (event locations, a new address), a few model-chosen times, deletes, and a rent increase above the account's historical maximum.
Four hijacked attacks were never stopped. Three are a string-matching artifact: the user wrote "June 13", so a file ID of "13" looked user-supplied. The fourth is real: the attacker has the agent email the user's passport and card numbers to the user's wife, at an address the user supplied. The recipient is legitimate and the harm is in *what* is sent.
The same gap shows up in real incidents. We analyzed four structurally, without reproducing payloads ([analysis](https://github.com/Yehielamor/provenance-gate/blob/main/poc/incidents_analysis.md