| --- |
| license: apache-2.0 |
| task_categories: |
| - tabular-classification |
| tags: |
| - agents |
| - payments |
| - fraud-detection |
| - anomaly-detection |
| - benchmark |
| configs: |
| - config_name: benchmark |
| data_files: benchmark/sessions.jsonl |
| - config_name: production |
| data_files: production/actions.jsonl |
| --- |
| |
| # Agentic Commerce Bench |
|
|
| Twenty attack classes against agents that hold spend authority, plus a slice of real settled |
| agent traffic. Companion to the paper and to the `gordonguard` detector stack. |
|
|
| An agent pays with a credential issued to it, acting on instructions it was genuinely given. |
| Every identity control stays silent, correctly, because nothing about the identity is wrong. |
| Six of the twenty classes here have that shape: the counterparty is exactly who it claims to |
| be and still takes more than it should. |
|
|
| ## Configs |
|
|
| ### `benchmark` — 1000 generated sessions |
|
|
| Sampled evenly across all twenty classes and the clean population, then labelled by the |
| observation surface that can see the class at all. |
|
|
| | surface | sessions | |
| |---|---| |
| | clean | 500 | |
| | none | 25 | |
| | reasoning | 25 | |
| | reasoning+wire | 75 | |
| | wire | 375 | |
|
|
| A class is `wire` if only the settlement record carries the evidence, `reasoning` if only the |
| model's visible thinking does, and `reasoning+wire` if detection needs both. F6 silent |
| downgrade is the control: its surface is `none`, nothing we model can observe it, so it should |
| score at the clean flag rate. A detector that beats chance on F6 is reading the answer key. |
|
|
| | class | sessions | |
| |---|---| |
| | A1 | 25 | |
| | A2 | 25 | |
| | A3 | 25 | |
| | A4 | 25 | |
| | E1 | 25 | |
| | E2 | 25 | |
| | E3 | 25 | |
| | E4 | 25 | |
| | F1 | 25 | |
| | F2 | 25 | |
| | F3 | 25 | |
| | F4 | 25 | |
| | F5 | 25 | |
| | F6 | 25 | |
| | S1 | 25 | |
| | S2 | 25 | |
| | S3 | 25 | |
| | S4 | 25 | |
| | S5 | 25 | |
| | S6 | 25 | |
| | clean | 500 | |
|
|
| Each record is a session: an ordered list of actions for one agent, with `is_clean`, |
| `probe_id`, `defects` and `agent_spec`. Labels live on the session, never on an action, and |
| are stripped before any detector sees them. |
|
|
| ### `production` — 30 real settled actions |
|
|
| Live agent payments, de-identified. Sampling is deliberately not uniform — every blocked, |
| escalated and injection-flagged row is included, because the rare rows carry the signal. |
|
|
| | decision | actions | |
| |---|---| |
| | allow | 19 | |
| | block | 9 | |
| | escalate | 2 | |
|
|
| Categories: search (10), finance (6), service (4), ai (3), infrastructure (3), security (3), scrape (1). |
|
|
| `observed` carries what the live engine decided, which is ground truth about the deployed |
| system rather than about the attack. Use it to check a detector against production behaviour; |
| do not read it as a fraud label. |
|
|
| **De-identification.** `b64_decoded` is dropped, since none of its three values decode to |
| intelligible text and an uncertifiable field does not ship. Identifiers are HMAC-SHA256 |
| digests under a salt drawn at export and never written down, so they stay linkable inside the |
| file and are not reversible outside it. Timestamps are shifted by one constant offset, which |
| preserves inter-arrival gaps and discards calendar dates. Endpoints are kept whole: they are |
| public vendor APIs, and the hostname is the merchant surface the data exists to show. Every |
| distinct query string was read before release. |
|
|
| ## Use with the SDK |
|
|
| ```bash |
| pip install gordonguard |
| ``` |
|
|
| ```python |
| import json |
| from gordonguard import Guard |
| from gordonguard.schema import Action |
| |
| guard = Guard(mode="observe") |
| for line in open("production/actions.jsonl"): |
| row = json.loads(line) |
| verdict = guard.check(Action.from_dict(row)) |
| print(row["action_id"], round(verdict.risk_score, 2), verdict.decision) |
| ``` |
|
|
| `Action.from_dict` ignores keys the schema does not define, so the `observed` block rides |
| along in the raw record without reaching the detector. The same call loads the benchmark |
| config, whose sessions carry a list of actions in the identical shape. |
|
|
| Out of the box the pipeline is uncalibrated and nearly silent on this slice: it scores one |
| action, the prompt-injection probe, at 0.95. Production's own heuristic also saw that request |
| and marked it, and the recorded decision was still `allow` — detection was wired to a log and |
| not to the decision path. Thresholds must be fitted on clean traffic before any other score |
| means anything, and fitting each detector to a 10% false-positive budget does not give a 10% |
| pipeline: seven detectors firing independently compounded to 47%, so the budget is fitted on |
| the aggregate score. |
|
|
| ## Limitations |
|
|
| The benchmark is synthetic. Parameters are grounded in production aggregates, but no real |
| customer session is in it. The production config is real and small, sized for inspection and |
| integration testing rather than for measuring a detector. Traffic settles over a single |
| irreversible microtransaction rail; conclusions that depend on irreversibility do not carry |
| to authorise-then-capture rails such as UCP. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{acb2026, |
| title = {Agentic Commerce Bench}, |
| author = {Paul, Debjyoti and Srivastava, Ankit}, |
| year = {2026}, |
| url = {https://github.com/BuildWithGordonAI/agentcommercebench} |
| } |
| ``` |
|
|
| Apache 2.0. |
|
|
