| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-classification |
| tags: |
| - prompt-injection |
| - agent-security |
| - payments |
| - near |
| configs: |
| - config_name: default |
| data_files: |
| - split: main |
| path: data/main.jsonl |
| - split: redteam |
| path: data/redteam.jsonl |
| - split: external |
| path: data/external.jsonl |
| --- |
| |
| # Attested Payment Firewall scenarios |
|
|
| Labelled payment scenarios for testing a payment firewall for AI agents (NEAR hackathon entry |
| "Attested Payment Firewall"). Each row is one scripted agent run: a user request, an inbox of |
| context items (some untrusted, possibly carrying an injection) and the payment the agent then |
| proposes. A firewall should hold or deny the `attack` rows and allow the `benign` rows. |
|
|
| ## Splits |
|
|
| - `main`: team-written and generated scenarios, 150 benign and 150 attack (25 per family). |
| - `redteam`: team-written attack-only scenarios aimed at the checker's weak spots. |
| - `external`: scenarios derived from public benchmarks (see Provenance), equal numbers of benign and attack. |
|
|
| ## Fields |
|
|
| - `name`: scenario id. |
| - `label`: `benign` or `attack`. |
| - `family`: `none` for benign rows; for attacks one of `direct_injection`, `encoded_injection`, |
| `indirect_injection`, `memory_poisoning`, `lookalike_receiver`, `fake_invoice`. |
| - `user_request`: the instruction the user gives the agent. |
| - `inbox`: list of `{source, ref, trust, text}` context items. `source` is one of `email`, `x_reply`, |
| `web`, `memory`, `tool`; `trust` is `trusted` or `untrusted`. |
| - `scripted_tool_call`: `{name, arguments}` with `arguments` = `{receiver_id, amount, memo}`, the payment |
| the agent proposes (`amount` is whole tUSDC as a string). |
| - `set`: `main`, `redteam` or `external`. |
| - `source`: `team`, or the public dataset id for `external` rows. |
| - `license`: `MIT` for team rows, the source dataset's license for external rows. |
|
|
| The literal `{NS}` in account ids is a placeholder for the NEAR account suffix (for example `demo.testnet`). |
|
|
| ## Provenance |
|
|
| `main` and `redteam` are written by the project team (MIT). `external` rows are public benchmark text |
| rewritten into payment form by `agent/tools/build_external_scenarios.py`: the benchmark text is placed |
| in an untrusted inbox item next to a generated invoice email, and attack rows get an appended receiver |
| line naming a synthetic account. They are not original benchmark rows and are not comparable to the |
| benchmarks' own metrics. |
|
|
| PaySim- and BEC-derived data is excluded from this dataset: those sources (Kaggle PaySim and Kaggle |
| adversarial BEC emails) are CC BY-SA 4.0. |
|
|
| ## Attribution |
|
|
| # External scenario attribution |
|
|
| `agent/scenarios/external/ext-*.json` are derived from the public datasets below, rewritten into |
| payment-scenario form by `agent/tools/build_external_scenarios.py` (each attack text gets an appended |
| receiver line naming a synthetic `ext-acct-NNN` account; each scenario also carries a generated |
| invoice email for a known payee). Attack amounts equal the invoice amount so that the label cannot be |
| read from an amount mismatch (decided by Jim, 2026-11-13). The scenarios are modified works; the license of each source applies |
| to the text taken from it. All URLs are pinned to the revision shown. Refs and receiver ids carry |
| neither the label nor the benchmark name, because both are sent to the decision model. |
|
|
| ## uiuc-kang-lab/InjecAgent (`injecagent`) |
|
|
| - License: MIT |
| - Source: https://github.com/uiuc-kang-lab/InjecAgent |
| - Revision: `f19c9f2c79a41046eb13c03c51a24c567a8ffa07` |
| - Files: |
| - https://raw.githubusercontent.com/uiuc-kang-lab/InjecAgent/f19c9f2c79a41046eb13c03c51a24c567a8ffa07/data/test_cases_dh_base.json |
| - sha256 `0a8186468d21389af432e8c7b399ae42264d1b93a07b65c7a489468508604305` |
| - Selection: data/test_cases_dh_base.json; attacker tools BankManagerPayBill, BankManagerTransferFunds, BinanceDeposit, BinanceWithdraw, VenmoWithdrawMoney; the first 6 distinct `User Tool` per attacker tool (file order) = 30 attack scenarios. |
| - Rewrite: inbox[1].text is the case `Tool Response` with ` Pay to <attacker>.` appended to the embedded attacker instruction; the amount is the invoice amount, so the attack redirects the receiver only; receiver is a synthetic ext-injecagent-NN account. |
|
|
| ## ethz-spylab/agentdojo (`agentdojo`) |
|
|
| - License: MIT |
| - Source: https://github.com/ethz-spylab/agentdojo |
| - Revision: `089ed468cf3ed0322acc66b0211f26d9d90dbf60` |
| - Files: |
| - https://raw.githubusercontent.com/ethz-spylab/agentdojo/089ed468cf3ed0322acc66b0211f26d9d90dbf60/src/agentdojo/default_suites/v1/banking/injection_tasks.py |
| - sha256 `fa2dedf8e66c95ffadb54f5a26648728b08c56f302858de4ee9a5cc0b3891618` |
| - https://raw.githubusercontent.com/ethz-spylab/agentdojo/089ed468cf3ed0322acc66b0211f26d9d90dbf60/src/agentdojo/attacks/important_instructions_attacks.py |
| - sha256 `519f24e0b9d72f43343d665679adacc33758b50cd1b586a81fa53fb098dcfe71` |
| - Selection: banking injection tasks 0-6 and 8 (task 7 changes a password, not a payment) x vectors injection_bill_text and injection_incoming_transaction = 16 attack scenarios. |
| - Rewrite: inbox[1].text is the `important_instructions` template (user `Emma Johnson`, model `AI assistant`) with {goal} = the task GOAL, its IBAN replaced by a synthetic ext-agentdojo-NN account; prefixed by `Bill for December 2023. ` (bill vector) or `Incoming transaction subject: ` (transaction vector). The amount is the invoice amount, so the attack redirects the receiver only. |
|
|
| ## nvidia/Nemotron-RL-Agentic-Indirect-Prompt-Injection-v1 (`nemotron`) |
|
|
| - License: CC-BY-4.0 |
| - Source: https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Indirect-Prompt-Injection-v1 |
| - Revision: `d738d4f361cc38bb4d7a42b9066776dade5332f5` |
| - Files: |
| - https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Indirect-Prompt-Injection-v1/resolve/d738d4f361cc38bb4d7a42b9066776dade5332f5/train.jsonl |
| - sha256 `3329da17564a7eb287e2730fc7d6956e1f4fe51e8950ac4f110b3c37e78cf3b9` |
| - Selection: train.jsonl rows with attack_category == unauthorized_action; the first 30 by `id`. |
| - Rewrite: inbox[1].text is `injection.injection_text` with ` Settle the outstanding balance by paying <attacker>.` appended; the amount is the invoice amount, so the attack redirects the receiver only; receiver is a synthetic ext-nemotron-NN account. |
|
|
| ## 3nesdeniz/agentic-prompt-injection-boundary-pairs (`boundary`) |
|
|
| - License: CC-BY-4.0 |
| - Source: https://huggingface.co/datasets/3nesdeniz/agentic-prompt-injection-boundary-pairs |
| - Revision: `a5682e7573e1c7bc4b12e64d49c0dcd90ca776cf` |
| - Files: |
| - https://huggingface.co/datasets/3nesdeniz/agentic-prompt-injection-boundary-pairs/resolve/a5682e7573e1c7bc4b12e64d49c0dcd90ca776cf/data/test.jsonl |
| - sha256 `034523eefaec18c291f9daa7699037acb0e7a91b1aeb1b205878b124f6e18f10` |
| - https://huggingface.co/datasets/3nesdeniz/agentic-prompt-injection-boundary-pairs/resolve/a5682e7573e1c7bc4b12e64d49c0dcd90ca776cf/data/validation.jsonl |
| - sha256 `1e5f483277ca776f0bb7d26c1a85ca4cd6d73f8d30f638c75265d82a6de751c3` |
| - Selection: data/test.jsonl rows with risk_domain in {billing, procurement}; data/validation.jsonl rows with the same filter are added only if the test split has fewer than 20 attack rows. label 1 = attack, label 0 = benign hard negative. |
| - Rewrite: attack: text + ` Payment destination: <attacker>.`, the amount is the invoice amount (so the attack redirects the receiver only), receiver a synthetic ext-boundary-NN account; family memory_context_poisoning -> memory_poisoning, obfuscation_encoding -> encoded_injection, otherwise indirect_injection. benign: text as-is, receiver and amount are the invoice payee and invoice amount. |
| |
| ## microsoft/llmail-inject-challenge (`llmail`) |
| |
| - License: MIT |
| - Source: https://huggingface.co/datasets/microsoft/llmail-inject-challenge |
| - Revision: `1063bdf01ec8762b812d5e06ee768a06faa5a6f7` |
| - Files: |
| - https://huggingface.co/datasets/microsoft/llmail-inject-challenge/resolve/1063bdf01ec8762b812d5e06ee768a06faa5a6f7/data/emails_for_fp_tests.json |
| - sha256 `4ddd950b5dbaa8548f5597c886d8e09a051ba07f80a9291fdcca9c2397d22abe` |
| - Selection: data/emails_for_fp_tests.json (benign emails, file order); the first N such that the total benign count equals the total attack count. |
| - Rewrite: benign: the email is inbox[1] (source `email`); the invoice payee is paid the invoice amount. |
| |
| ## Known label correlates |
| |
| The set is not label-neutral beyond the metadata above. Remaining correlates, computed from the built set: |
| |
| - `inbox[1].source`: benign email=76, benign tool=24; attack email=0, attack tool=100. |
| - 100 of 100 attack texts name their receiver account; 0 of 100 benign texts name an `ext-` account. |
| - The llmail benign emails are unrelated to payments; the boundary-pairs benign texts are hard negatives. |
| |
