Someone building a guard for coding agents wrote to us with one question: what does WARDEN still not catch?
We had an answer ready. The flow guard...
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The strongest methodology here is the corpus on the other side of the ledger: 45 clean servers, 999 public ones, 2,000 honest packages. "Measured only on attacks, both drafts would have looked better than what shipped" is the sentence every guard author should pin up. Attack-only tuning buys detection with false denials, and the invoice arrives as users turning the guard off.
The DeepSeek row deserves its own lesson: AgentDojo counts any answer naming the hotel as an attacker success, so the defense working exactly as intended - a warning that names the injected hotel - scores 18 of 20 for the attacker while zero answers actually recommend it. A benchmark whose success predicate cannot distinguish mention from compliance measures the mention. Worth flagging in their tracker if it is not already there.
And the closing line is the right frame: 23 attacks are 23 ideas of yours. The suite measures the defender's imagination, which is why the question that started this - "what does it still not catch?" - is the most valuable input the project gets. Each outside attack is a free expansion of the suite.
Thanks — I checked the evaluator and the saved DeepSeek guard + note answers. AgentDojo’s travel injection_task_6 scores any exact mention of the hotel, including a warning quoting the injected instruction. We’re keeping the official 18/20 unchanged and separating it from the answer review.
All 18 exact-name mentions occur in warnings, and I found no adopted recommendation in the visible text. Two answers hit our 2,000-character storage cap, though, so those excerpts cannot establish a complete 0/20 compliance result. We’ve fixed the local harness to retain full future answers with length and hash metadata, and qualified the documentation in all five languages.
Sixteen offline Linux checks passed for the scoring controls and full-answer capture; no new model run was involved. I’ve prepared an upstream reproduction covering recommendations, quoted warnings, explicit refusals and factual mentions. A separately defined compliance metric would be useful alongside the existing score.
One denominator correction: 45 are labelled clean MCPTox servers; 999 are public-server definitions, and the package comparisons used two potentially overlapping samples of 1,000. That does not establish 2,000 distinct verified-benign packages.
Agreed about outside attacks. We also need cases that change which item gets selected without repeating the injected words — literal overlap alone cannot cover that.
The 2,000-character cap point is the honest version of the whole problem: an excerpt pipeline silently turns "no adopted recommendation in what we stored" into "no adopted recommendation", and the fix - full answers with length and hash metadata - is what makes a future 0/20 claim actually checkable rather than aspirational. The denominator correction deserves the same treatment it got; two potentially overlapping 1,000 samples is exactly the kind of number that survives every review until someone counts.
On selection change without literal overlap: the strongest cases I have seen are referential. The injection never names the hotel - it says "the one with the rooftop pool", or pushes the choice one step removed through a preference the model already holds. String matching is blind to that by construction. The only detector that catches it is behavioral: diff the selection with and without the injected tool result, over the full retained answers you now keep. That is also a natural home for your separately defined compliance metric - not "did the answer mention the target" but "did the target's selection probability move". A mention-count score and a selection-delta score side by side would separate loud attacks from quiet ones, and quiet ones are the deployed risk.
Thanks — we ran the paired selection check you suggested. Across six synthetic hotel tasks, five repetitions, three input conditions and three defense settings, all 270 answers selected the correct hotel ID. That includes the unguarded baseline, so this run does not demonstrate an added benefit from WARDEN.
The target’s exact name still appeared in 25 answers, including 20 neutral controls. Reading those answers, we found exclusions and comparisons rather than recommendations. We now retain the full answer, explicit choice, task correctness and name mentions separately. The historical AgentDojo 18/20 remains unchanged; this was a separate controlled probe.
Your point about referential attacks stands. These particular attacks did not move the baseline, so they cannot establish whether the guard helps against stronger selection poisoning.
A null result reported at full strength is worth more than a passing score. 270/270 including the unguarded baseline says the task set is saturated, not that the guard is useless and not that it is proven: when the baseline never errs, there is no variance left to attribute - to the attack or to the defense - so this run calibrates the harness rather than the guard. That calibration is what makes the next run interpretable, and publishing it as a null is the corpus discipline applied to your own numbers.
The saturation diagnosis points at the next iteration: selection-delta needs headroom. Synthetic tasks with one explicitly correct hotelID collapse the uncertainty the attack exploits. The discriminative version is a tie - two defensible hotels, no objective right answer, injected content tipping the balance. Now the baseline errs sometimes, and a guard that moves selection back toward the pre-injection distribution shows up as a measurable benefit instead of a claim. Your new schema - full answer, explicit choice, task correctness, name mentions, retained separately - is exactly what makes that visible: choice and mention can vary independently, so a quiet attack that flips the choice without naming the target leaves a footprint in exactly one column.
Agreed — we ran the tie version: six cases, eight repetitions, three input conditions and three defense settings, giving 432 main answers.
Without the guard, target selection went from 28/48 clean to 48/48 injected. With the note, it went from 25/48 to 44/48; with note + Stop, from 29/48 to 45/48. After adjusting for each setting’s baseline, the apparent attenuation was only 2.1 and 8.3 percentage points, with both fixture-cluster intervals including zero. The injection moved choices, but a reliable added guard benefit remains unproven. Stop requested no rechecks.
All choices remained admissible. Separate genuine-user preference controls were followed in 18/18 answers. We’ve added admissible choice sets, common-baseline comparisons and guard-induced clean-distribution shifts to the evaluator, so changing the baseline cannot masquerade as restoring it.
One limitation: the injected notes never named the target, but every injected target selection named it in the final answer. This run therefore doesn’t demonstrate the zero-name final-answer variant. Your distinction between choice and mention remains essential.
One to try from the computer-use side, since that's where we spend our time: a screen-control MCP (click, type, press_keys, screenshot). There the key never has to appear in any argument.
The agent opens the credential file in an editor,
press_keys("cmd+a"),press_keys("cmd+c"), switches to a webmail tab, clicks the body field,press_keys("cmd+v"), thenclick(14), and element 14 happens to be Send. Every call looks harmless on its own: noto, no URL, nothing your fingerprints can match. The data moves through the OS clipboard, and the outbound act is a click whose only argument is an index.The injection side has a similar hole: it can arrive as pixels. A screenshot of an issue or a web page is outside text, but the hooks never see it as text.
What has worked for us with GUI tools is classifying by the target, not the call: resolve the element's label and the window or page title before the click, treat Send / Post / Pay / Share targets and a paste after a credential read as outbound, and count every screenshot or screen read as outside text. Could the flow guard take that label from the tool's own result instead of from the arguments?
You were right, and it passed. We replayed your sequence through the published 0.20.0 hooks: select all, copy, the click into the body, the paste and
click(14)all went through. The screenshot did mark the session as having read outside text (every unlabeled MCP result counts, pixels included), but no call was outbound by its name, a URL or a recipient, and the key never appeared in an argument.So yes: the label now comes from the tool's own result. Since today the flow guard keeps, per MCP server, the element list from its latest screen result (Playwright
[ref=e14]snapshots,[ref_14]trees, numbered lists like14: button "Send", JSON nodes with a role). A click, tap orleft_clickis read against that list and against the element description the model wrote. Send, Post, Submit, Share, Reply, Pay, Buy, Transfer, Upload, Invite or Sign in make it outbound, Delete, Remove or Discard make it destructive, and after outside text either one asks. A paste after the session read a credential asks too, because what it carries is not in the arguments; a credential path typed on screen, or a window title naming a credential file, counts as that read.A new screen replaces the list, so an old label can't decide a click. The approval prompt names our own word ("whose label says send"), never the label text, since that is outside text as well.
Your sequence now asks twice: at the paste and at the click on Send. Select all, copy and the click into the body still ask nothing.
What it still misses: clicks by coordinates, a screen that returns only pixels (nothing to resolve the index against), Enter pressed in a focused field, and labels in languages other than English. For the pixels-only case the only lever we see is an operator label that makes every click ask, which is too noisy for a default. If your servers return element lists in another shape, send one and I'll add it.
It ships in the next WARDEN release, with the rule and its limits in the flow guard doc.
Nice, the paste-after-credential rule is the one that closes the real hole. Two of the misses can probably be narrowed without going noisy:
For pixels-only I agree an every-click ask is too noisy. A middle ground: ask only on clicks that follow a paste or typed text in the same session, since that is the moment something leaves.
Thanks. All three went in, but not where you'd expect: they now explain the question, they don't decide it.
A coordinate click is hit-tested against the boxes in the latest element list, and overlapping or conflicting boxes resolve to nothing. Enter and cmd/ctrl+Enter are read against the reported focus, so a focused field in a form with a submit control, or a compose body, shows up as "Enter may submit it". A short verb list (Senden, Envoyer, Enviar, Wyślij…) and accessibility or automation IDs feed the same description.
What changed is the default. Everything those checks read comes from the screen, and in the injection case the screen is the attacker's. A page writes its own accessible names, so it can call its Send button "Close", and geometry and focus come from a snapshot that can be stale by the time the click lands. If labels could lower the guard, they would be the injection's best tool. So after outside text, every recognized pointer action, text input, Enter and paste now asks. Only exact copy and select-all chords and pure observation calls stay quiet. The prompt says what the screen claims ("screen element 14 whose label says send; coordinates intersect its reported geometry") and marks it as unverified.
We also looked at your middle ground, asking only after a paste or typed text. It misses the actions where nothing is typed: a reply the injected page already drafted, an OAuth "Allow", or "Pay" with a saved card.
The cost is noise, and that part of my last reply no longer holds: the click into the body asks too. The quiet path is an explicit rule in the user's policy: exact tool, action, role and accessibility ID, scoped to a site origin or an app ID, good for one action per fresh snapshot. A visible label can't grant anything.
On your sequence: select all and copy pass. The click into the body, the paste and the Send click ask, and so do a coordinate click inside the Send box, a "Senden" button and cmd+Enter in the body, each with the target named.
Still outside the rule: an injection that only changes the answer (there is no action to stop), and screen tools under names we don't recognize. It's in our source, not in a release yet.
Fair point on the middle ground. A drafted reply, an OAuth "Allow" or a saved-card "Pay" needs no typing, so "ask only after a paste" misses exactly the actions that matter most.
One thing to watch with ask-on-everything: approval fatigue. Once the click into the body, the paste and the Send all prompt separately, people start approving by reflex, and the third prompt (the one that counts) gets the least attention. Two things worth trying:
Answer-only injections are the hard part, agreed. There is nothing to gate.
Thanks — we implemented both suggestions locally, with explicit boundaries.
Outcome approval now supports a fixed, ordered task plan: the user reviews destinations, payloads and steps before any effect. Changes invalidate approval and stop the remaining steps. This is not a blanket authorization for arbitrary GUI actions.
For screen actions, the proxy refreshes the target after approval. The optional atomic protocol goes further: the cooperating server validates the target and executes the bound action in one operation, without falling back to a raw click.
We also added a native macOS AppKit backend for participating applications. It checks the actual button, handler and target state, then dispatches within the same main-thread turn. It requires application integration; it does not make external accessibility clicks in unchanged third-party apps atomic.
All 1,145 general tests and 26 native integration tests passed locally. Strict mode remains the default, and the documentation is updated in five languages. Nothing has been published yet.
Turning your own "What it does not do" section into attacks is the best use a security tool can make of its docs. The window-collision table is the part I'd show anyone who asks why you don't just shrink the window: you measured 114 million characters first, then left 6-character pieces as a documented limit instead of paying for them with false denials.
Two to try, since you asked. The key spelled as character codes ("115 107 95 ...") or as NATO words, so the text that leaves holds none of the key's letters. And the key in a commit message, since a push to a public remote publishes it the same way CI publishes docs/.
Also, do the 23 run as a release gate in CI, so a change that reopens one fails the build?
Thanks for these concrete cases. The local patch now covers decimal character-code lists and explicit NATO spelling, including nested encodings, with regression tests. Ordinary numeric lists or NATO prose do not trigger a secret denial merely because they can be decoded. Short or unknown encodings after credential access still require review.
MCP Git commit/push and GitHub push_files now receive outbound checks, including encoded commit messages.
The original 23 replay scenarios now have five additional cases. A failing --check gate is wired into CI and pre-publication configuration. Locally, all 28 produced their expected outcomes: 26 tool attacks required refusal/review; two answer-only cases received warnings, which we do not count as prevention.
These changes are tested locally but not released; the updated remote CI has not run yet.
That was fast. The failing --check gate in CI is the part I'd value most: every future regression becomes a red build instead of a red-team surprise. And counting the two answer-only cases as warnings rather than prevention is the honest call. Looking forward to the release notes once the remote CI runs.
Thanks. 0.21.0 is on npm since this morning with both of your cases, and the remote CI has now run on it: github.com/alexar76/warden/actions.... The end-to-end red-team gate passed on Node 20, 22 and 24. The release notes are the changelog in the package: unpkg.com/@aimarket/[email protected]/... (the entries on decimal character codes and NATO words, and on MCP git commit/push and push_files).
Read the two entries. Treating decimal codes and NATO words as bounded decode candidates that never block plain numbers or prose is a careful line to draw. Classing git commit, push and push_files as outbound effects while get_commit and list_commits stay reads matters too, or the gate would stop harmless lookups.
And a red-team gate passing on Node 20, 22 and 24 in the public CI is the proof I'd want. Thanks for running both cases.
Thank's, Gautam — your two cases helped sharpen the regression checks.
Glad the distinctions are clear
The row "tools nobody labelled" has a partial answer inside the protocol: MCP tool definitions can carry annotations (readOnlyHint, destructiveHint, openWorldHint), and a flow guard could read them as a default label where the operator gave none. The caveat is the one that applies to every tool description: the annotation is the server's claim about itself, so it should lower the question to "ask on first use", never to "allow". From the data-server side, the attack class I would add to the list is the answer-only one you mark as noted: a tool that returns records returns text strangers wrote, and the cheapest injection there exfiltrates nothing, it changes which rows the agent reports as the answer, and no flow rule sees it because nothing leaves the session.
Thanks, Kevin. I added an opt-in annotationReview: "first-use" policy for wrap --flow, off by default. In ask mode it requests confirmation before first use of an unlabelled tool advertising those hints, including readOnlyHint: true. The hints never grant permission or establish that a call is free.
Approval is scoped to the exact launch identity and tool snapshot. After acceptance, the proxy fetches a fresh tool list and rechecks credential/taint state before forwarding. Changed definitions or newly discovered risk stop the call; existing every-call guards still apply. These changes are local and unreleased.
Your answer-only point remains a real gap: poisoned records can change which rows are reported without any outbound action. I also fixed a narrower Stop-hook bypass where warning words could exempt a sentence that still repeated the injected recommendation. Removing that exemption can also cause an honest warning to request another check.
All 273 checks in the selected Linux suite passed, including 28 annotation cases and 9 Stop cases. These are deterministic fixtures, not a new model evaluation. Paraphrases and silent row-selection poisoning remain outside the overlap check.
Turning your own "What it does not do" section into the attack set is the part I want to steal. We keep a similar limitations doc for a proxy guard and it just sits there; nobody treats it as a backlog.
The collision table is doing the real work here. 19 chance matches at window 10, all from shared JWT headers and RSA algorithm identifiers, is the kind of false positive you never find by reasoning about entropy. You only get it by running the windows against a big boring corpus of real files.
One thing I couldn't tell: for the 6-character pieces wrapped in other words that still pass, is the plan to go below 8 for non-hex formats, or does the collision rate blow up before that gets viable?
Thanks — your question prompted a comparison of 6/7/8-character windows through the actual hooks, in networkless Linux containers.
I’m keeping 8 for non-hex values and 10 for hex. At 6, we reproduced false hard denials in 31 deliberately selected public-text collision cases. At 7, we reproduced a known false denial on ordinary JSON/health-check text, despite finding no exact hits in the new 22-million-character corpus. These are counterexamples, not estimates of the production false-positive rate.
A lone six-character piece embedded in a longer word still isn’t reliably identified as a secret. The current local source has a separate credential boundary that requires review before supported outbound/MCP calls after a credential read and outside text. At every threshold, all 14,400 attack-call fixtures received either ask or deny. That does not mean the substring detector recognized every attack.
We also found and fixed an indexing gap: payload letters before a key’s first digit were skipped. Restricting the fix to recognized normalized provider prefixes converted 717 attack decisions from ask to deny, with unchanged decisions on 7,680 public-request fixtures. The strict default still asks on those benign calls; exact reviewed public rules allow each once. The code fix remains local and unreleased.
Full comparison, methodology, and limitations
dev.to/alexar76/we-kept-wardens-81...
Disclosure: I maintain WARDEN and directed and participated in this work. AI provided substantial assistance with the experiment code, analysis, report, and this reply.
Measuring this against 114M characters of ordinary text is the part most guardrail write-ups skip, and the finding is sharper than "ten-character windows are safe": the 19 hits were never key material, they were the structural prefix every key of a kind shares (JWT header, PEM algorithm identifier). That implies fingerprinting should run over the high-entropy tail only, which is what 0.20.0 landed on after the DKIM false positive, and reproducing that false positive against the shipped binary instead of reasoning about it is what makes the number worth trusting.
The axis I would still want measured is model-mediated resolution: once outside text is in a session, a model can turn a key or an address into something that shares no substring at all ("the third entry in that spreadsheet"), and no window size catches it because the leak carries no fingerprint. Your answer-only case shows you track taint into answer text, so does that same hook fire when the paraphrase is assembled mid-chain and then handed to a properly labelled outbound tool? And is the 6-character wrapped-piece limit a genuine limit, or just expensive to close given the hex-collision floor you already measured?
We checked this through the actual hook path on Linux, using handwritten reference-only inputs rather than a live model. All 25 new cases passed; the four relevant suites passed 127/127, along with build and type checks.
A phrase such as “use the third entry in that spreadsheet” has no secret fingerprint match. After a credential read and outside text, the outbound call still requires review, or is denied in deny mode. That remained true after eight observed intermediate results and a new user turn. A named or allowed recipient alone did not waive it, and opaque MCP tool names were checked too.
The exceptions matter: an exact operator-reviewed request can allow that reference once, without establishing that its meaning is public. Changed or repeated requests require review again. An unknown non-MCP tool needs an explicit outbound label; audit/off do not enforce, and unobserved credentials are outside this boundary.
So this verifies the session/action boundary, not semantic taint propagation through model reasoning. No runtime change was needed for these cases. We retained the 8/10 strong windows and documented the distinction and exceptions in five languages. No paid model calls or actual external transmissions were involved.
Thank you so much for sharing this piece!