Jim10/attested-payment-firewall-scenarios-eclipse

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.