SafePay

The payables firewall for AI agents. SafePay sits between an agent that pays vendors and PayPal, and decides, before any money moves, whether to allow, hold, or block the payment. It checks the payee on the live web, catches lookalike vendors and inflated invoices, holds what a person should see, pays the rest through PayPal Payouts, and leaves a signed link in a hash chain for every decision. And it is measured: a reproducible benchmark of 14 real attacks on a wallet-holding agent, not a claim.

Built for the PayPal AI Hackathon (Agentic Commerce and Autonomous Payments) by NovaCorp Labs.

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The problem

Agents are starting to pay the bills: SaaS renewals, cloud invoices, contractor payouts. The upside is obvious; the risk is the same thing. An agent that can pay can also be prompt-injected by an invoice email into paying an attacker, talked into an amount that does not fit its task, handed a real vendor's name on a lookalike domain, or sent to a merchant that was registered last week and shows up on scam-report pages. A static allow-list is too blunt, and letting the model decide alone is exactly what goes wrong.

SafePay is the missing layer: a payments firewall that an operator describes in plain words once, and that every agent payment has to pass through. Spending caps are where most tools stop. SafePay also looks outward (is this payee what it claims to be?) and inward (does this payment serve the task the agent was given?), and it proves how well that works.

How it works

Each PaymentIntent runs three checks, cheapest and strictest first:

1. Policy (deterministic). Per-transaction, daily, per-recipient and velocity limits, allowed and blocked categories, recipient allow/block lists, currency, and an approved vendor directory (per vendor: its real domains and the amount you expect to pay it). A breach here is a hard block, no model call spent. The daily, per-recipient and velocity caps are enforced across a burst of payments, not just one in isolation, so a slow-drain or high-frequency attack is caught. The vendor directory catches the two attacks that stay inside every cap: a lookalike recipient (real vendor name, swapped domain) and an inflated amount to a real vendor; a stated merchant URL must also agree with the recipient's domain. With hold_unknown_vendors on, any payee outside the directory is held for a human, the "new beneficiary" control banks apply. hold_above is "ask me before anything over X": inside the hard cap, but big enough to wait for a tap. The deterministic policy is the floor: the model layers below can tighten a verdict to HOLD, never loosen a policy block.
2. Merchant verification (live web, Tavily). Pulls fresh web evidence about the payee and flags fraud/scam signals. A soft signal: it raises risk and can trigger a hold, but never silently blocks on noisy web data.
3. Intent and anomaly reasoning (NVIDIA Nemotron on Nebius Token Factory). Reads the task the agent was given and judges whether the payment actually serves it. Catches goal/payment mismatches a rule set misses (a cloud-invoice agent paying a casino).

The verdict aggregates the three. On allow, SafePay releases the payment via the PayPal Payouts API (agent sends money, no buyer redirect, fully backend). On hold or block, the money never moves. Every decision is written to a tamper-evident, optionally HMAC-signed audit record, and when PayPal later reports what happened to the payout (succeeded, failed, returned) that settlement is appended to the same chain, so the record says not only what SafePay decided but whether the money actually moved.

PaymentIntent ─▶ [policy] ─▶ [merchant web] ─▶ [reasoning] ─▶ verdict
                    │                                              │
                 hard block                            allow ─▶ PayPal sandbox payout
                                                        hold/block ─▶ stop + audit

Run it

One minute to a first verdict, no keys needed:

pip install safepay          # or, from a clone: pip install -e .
safepay --demo               # 7 scenarios, payouts simulated, lookalike and caps blocked

Three lines to put it in front of your own agent's payments:

from safepay.agent import SafePayAgent
from safepay.models import PaymentIntent, PolicyConfig

agent = SafePayAgent(policy=PolicyConfig(per_tx_limit=500, daily_limit=2000))
result = agent.pay(PaymentIntent(recipient="billing@notion.so", amount=48,
                                 purpose="Team plan", task_goal="pay approved SaaS invoices"))
# result["decision"]["verdict"] is "allow", "hold" or "block"; on allow the payout went out.

From a clone, with the real checks:

pip install -r requirements.txt
# Simulated mode (no keys needed): policy checks run, payouts are simulated.
python -m safepay --scenarios examples/scenarios.json

# Full mode: copy .env.example to .env, fill the keys, load it, and re-run.
set -a; . ./.env; set +a
python -m safepay --scenarios examples/scenarios.json

Web console (what a judge runs)

set -a; . ./.env; set +a
python webapp/app.py
# open http://127.0.0.1:5000

Write the policy in plain words at the top and apply it, then load the demo scenarios or enter a custom payment and watch each intent resolve: the checks, the verdict, the PayPal sandbox batch, and the signed stamp. "Agents in this run" lists every agent seen with a Freeze / Resume switch. "Reset ledger" starts the daily-cap scenario fresh. Below the results, the audit ledger (an AG Grid table over /api/audit) shows the hash-chained log with a live verification badge; "Tamper (demo)" edits a row in place to show the chain break turn the badge red, and "Verify chain" re-checks it.

Hosted demo

Live: https://safepay.agentposhq.com (no login, no local setup). It runs on Vercel from this repo (api/index.py + vercel.json); every push to master redeploys. A second live instance runs on Render from the included blueprint: https://safepay-ygzp.onrender.com (render.yaml + the gunicorn entry point wsgi.py; free plan, so it may take up to a minute to wake after idling). Set the secret values (PayPal, Nebius, Tavily, signing key) as environment variables on the host; without the PayPal keys it runs in simulated mode. Locally the same server runs with gunicorn -w 1 -b 0.0.0.0:8000 wsgi:app.

Single intent from the command line

python -m safepay --recipient vendor@example.com --amount 42 \
  --category software --purpose "monthly API bill" \
  --goal "pay the team's approved SaaS invoices"

Add --json for the full decision and audit records.

Gate the PayPal Agent Toolkit (agentic commerce)

SafePay also drops in front of the PayPal Agent Toolkit. The toolkit gives an agent real PayPal tools (create_order, capture_order, and more); PayPal's own guidance is to "invoke pay_order only after the user approves." SafePay is that approver, made automatic: it judges the intent, and the toolkit's money-moving tool is called only on ALLOW.

python -m venv .venv-agent
.venv-agent/Scripts/pip install -r requirements.txt -r requirements-agent.txt
set -a; . ./.env; set +a
.venv-agent/Scripts/python examples/agent_toolkit_demo.py

Allowed intents create a real sandbox order (with its buyer approval link); over-limit and daily-cap breaches are stopped before the toolkit is ever called. The gate lives in safepay/agent_toolkit.py as GuardedToolkit:

from safepay.agent_toolkit import GuardedToolkit
gate = GuardedToolkit(policy=policy)          # reasoning/web optional
out = gate.create_order(intent)               # evaluate, then create on ALLOW
# out["decision"], out["result"] (the order), out["audit"] (signed)

The toolkit is an optional dependency; the core package and tests never need it.

Use it as an MCP server (any agent)

SafePay also runs as a Model Context Protocol server, so any MCP client (Claude Desktop, Cursor, an agent framework) can route its payments through the guardrail without importing any SafePay code. It exposes twelve tools: safepay_evaluate (judge without paying), safepay_pay (evaluate, then pay via Payouts only on ALLOW), safepay_create_order (gate the Agent Toolkit order flow), safepay_pending_approvals, safepay_approve and safepay_reject (the human in the loop for HOLD decisions), safepay_payout_status (settlement), safepay_draft_policy (plain words to a policy draft), safepay_agents, safepay_pause_agent and safepay_resume_agent (the kill switch), and safepay_audit_status (re-verify the tamper-evident chain).

pip install -r requirements.txt -r requirements-mcp.txt
python -m safepay.mcp_server          # stdio transport

Point your MCP client at that command. For Claude Desktop or Cursor (examples/mcp_config.json):

{
  "mcpServers": {
    "safepay": {
      "command": "python",
      "args": ["-m", "safepay.mcp_server"],
      "env": { "PAYPAL_CLIENT_ID": "...", "PAYPAL_SECRET": "...",
               "NEBIUS_API_KEY": "...", "TAVILY_API_KEY": "...",
               "SAFEPAY_SIGNING_KEY": "..." }
    }
  }
}

Run it from the repo directory (so safepay is importable), or pip install -e . first. The mcp package is optional; the core package and tests never need it.

Keys

Key	What it turns on	Without it
PAYPAL_CLIENT_ID / PAYPAL_SECRET	Real sandbox payouts	Payouts are simulated
NEBIUS_API_KEY	Nemotron intent/anomaly reasoning	Reasoning check is skipped
TAVILY_API_KEY	Live-web merchant verification	Merchant check is skipped
SAFEPAY_SIGNING_KEY	HMAC-signed audit records	Records are integrity-only (unsigned)
SAFEPAY_AUDIT_LOG	Persist the hash-chained log to this JSONL file	Log is in-memory only
SAFEPAY_EXECUTE	simulate/0/false evaluates without moving money (budget still charged)	ALLOW executes the payout
SAFEPAY_RATE_LIMIT	Max web-console evaluations per IP per minute	30
SUPABASE_URL / SUPABASE_SERVICE_ROLE_KEY	Durable, shared audit chain, ledger and approvals	In-memory state
PAYPAL_WEBHOOK_ID	Accept PayPal Payouts webhooks at /api/paypal/webhook (signature verified with PayPal)	Settlements only on demand (console button, API, MCP tool)
TELEGRAM_BOT_TOKEN / TELEGRAM_CHAT_ID / TELEGRAM_WEBHOOK_SECRET	Every HOLD is pushed to a Telegram chat with Approve and pay / Reject buttons; taps come back through /api/telegram/webhook	Approvals in the console, API and MCP only

Get PayPal sandbox keys at developer.paypal.com > Apps & Credentials > Sandbox > Create App. The host is pinned to the sandbox in config.py; SafePay never defaults to the live host, so it cannot move real money by accident.

The demo scenario

examples/scenarios.json runs one agent (ops-agent-7) whose task is to pay the month's approved software and cloud invoices. SafePay:

• allows the Notion subscription (within limits, aligned, reputable),
• blocks the same invoice sent to billing@notion-payments.example: a real vendor name on a lookalike domain, caught by the vendor directory before any model runs,
• blocks a USD 900 charge (over the per-transaction limit),
• holds a casino payout (reasoning catches the goal mismatch; a rule set alone would let it through),
• allows two cloud invoices, then
• blocks the next (it would breach the daily cap).

Catching prompt injection

set -a; . ./.env; set +a
python examples/prompt_injection_demo.py

An agent reads an invoice email carrying a hidden instruction to pay an attacker. The injected payment is deliberately inside every spending limit, so policy alone passes it, but the reasoning layer catches that a "processing fee to release invoices" does not serve the task, and holds it for a human, while the real invoice is allowed. This is the agentic-commerce attack SafePay exists to stop.

Attack benchmark (proof, not promise)

benchmark/attacks/ runs 14 realistic attacks on a wallet-holding agent (prompt injection in invoices and vendor pages, goal mismatches, an unknown payee, a lookalike recipient, amount inflation, limit breaches, a slow drain, a velocity burst) plus 7 legitimate invoices, through the guardrail. Three modes, side by side, so the numbers are honest:

Mode	Attacks caught	Legit allowed (no false positives)
policy only (rules + vendor directory, no model, no web)	6/14 (42%)	7/7 (100%)
full (policy + Tavily + Nemotron)	13/14 (92%)	7/7 (100%)
strict (full + hold unknown vendors)	14/14 (100%)	7/7 (100%)

Rules alone catch what breaches a limit plus, with the vendor directory, the lookalike recipient and the inflated amount (the two misses of the first version of this benchmark, now closed deterministically). The model layers catch every prompt injection and every goal mismatch, with zero false positives on real vendors. The one case full mode does not catch reliably is the unknown payee with a plausible invoice: the model checks are not deterministic and that case flips between runs (caught by reasoning in an earlier run, missed in the three runs behind the table). That is exactly what the hold_unknown_vendors control is for, and strict mode shows it. Full per-case results are in benchmark/attacks/results.md.

python benchmark/attacks/run_attack_benchmark.py --mode all    # needs keys for full/strict
python benchmark/attacks/run_attack_benchmark.py --mode policy # offline

Policy in plain words

An operator should not have to write JSON to say what they mean. In the console (and with safepay --draft-policy "..." or the safepay_draft_policy MCP tool) you write the rules as you would say them:

Up to $300 per payment and $1,000 a day. Ask me before anything over $100. No gambling or crypto. Hold any new payee for me.

Nemotron turns that into a PolicyConfig draft (a rule-based parser does it without a key), with a one-line summary per rule and a question for anything important the text left unsaid. You review the exact fields, edit them if you like, and apply. Three things keep this honest: the model's output is data that is validated and coerced field by field (unknown fields dropped, caps clamped, a hold_above over the hard cap turned off); the model never applies anything, a person does; and text that tries to instruct the reviewer ("ignore the limits") is refused before any model reads it. The applied policy belongs to your console run and follows it: the agents in that run are judged by it, other sessions are not.

Human in the loop (HOLD is a workflow, not a dead end)

When a soft check raises a concern inside the policy limits, or a payment is over the operator's "ask me" threshold, the verdict is HOLD and the payment waits in an approval queue. In the web console a "Pending approvals" panel appears with Approve and pay / Reject; the same queue is available as GET /api/approvals, POST /api/approvals/<id>/approve and /reject, and as MCP tools, so a person (or a supervising agent) can decide from anywhere. With a Telegram bot configured, every HOLD also lands in your chat as a card with the same two buttons, and the tap resolves it through the same path, so the chain records "approved by human: via Telegram by ". Approving releases the payment and charges the spend ledger, so the daily and per-recipient caps reflect real spend; rejecting moves nothing. Both outcomes are appended to the audit chain as their own link, so "who approved this agent payment, and when" is provable.

Telegram setup, once: create a bot with @BotFather, message it, then python -m safepay.telegram --chat-id to read your chat id, set TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID and TELEGRAM_WEBHOOK_SECRET (--new-secret makes one), and register the webhook with python -m safepay.telegram --set-webhook https://<host>/api/telegram/webhook. Telegram echoes the secret in a header on every delivery; anything else is refused.

Freeze an agent (the kill switch)

A bank lets you freeze a card the moment something looks wrong; SafePay does the same for an agent. Every agent id the guardrail has seen is listed in the console with its status, how many decisions it asked for and when it was last seen. Freeze agent is the kill switch: the agent's next request is refused before any check runs (a frozen agent is the one rule that beats every other), the refusal is chained like any decision, and nothing moves until Resume. The same controls are GET /api/agents, POST /api/agents/<id>/pause and /resume, and the safepay_agents, safepay_pause_agent and safepay_resume_agent MCP tools, so a supervising agent or an on-call engineer can freeze a misbehaving agent from anywhere. With Supabase configured the registry is shared by every instance of the service.

Settlement (did the money actually move?)

PayPal accepts a payout as PENDING; it succeeds, fails or is returned later. SafePay closes that loop. POST /api/payouts/<batch_id>/settle (the "check settlement" button on a paid card in the console, the safepay_payout_status MCP tool, or SafePayAgent.settle()) asks the Payouts API for the batch's real state and appends it to the chain as a settlement link: SUCCESS is an ALLOW link (money moved), a final failure is a BLOCK link that also releases the budget the payout had charged (the money never left, so the cap should not count it), anything still in flight is a HOLD link. Settling twice with the same state appends nothing.

With a webhook, PayPal pushes the event instead: create one in the developer dashboard for PAYMENT.PAYOUTSBATCH.* and PAYMENT.PAYOUTS-ITEM.* pointing at https://<your host>/api/paypal/webhook, and set PAYPAL_WEBHOOK_ID. Every delivery is verified with PayPal's signature API before it is acted on, and even then only the batch id is taken from the event: the state is re-read from the Payouts API, never trusted from the body. The hosted demo has this webhook live: a payout released there gets its settlement link from PayPal's push, no button needed. If a delivery fails verification, the reason PayPal gives (for example a wrong PAYPAL_WEBHOOK_ID) is printed to the host logs.

Durable state (Supabase)

By default the spend ledger, the audit chain and the approval queue live in memory, which is fine locally and in tests but not for a hosted service: a restart loses them, and on a serverless host each instance would keep its own copy. Set SUPABASE_URL and SUPABASE_SERVICE_ROLE_KEY and all three persist in Supabase and are shared by every instance:

• the chain is appended against the head stored in the database (seq is the primary key, so two instances can never both claim the same link),
• the daily cap, per-recipient cap and velocity rule are computed from real history over a true UTC day,
• approvals survive restarts and resolve atomically,
• the per-run policy and the agent registry (freeze / resume) are shared too (safepay_settings, safepay_agents).

Create the tables once with supabase/schema.sql (RLS on, no policies: only the service role key, kept server-side, can touch them). The console shows state: durable (Supabase) when it is on. The audit chain is never deleted; "Reset ledger" in the console starts a new run id for the demo agents instead.

The console also has an Early access form (POST /api/early-access) for teams that want SafePay in front of their own agents' payouts. Sign-ups go to the same backend (safepay_leads, upserted on email, in memory without Supabase). The endpoint is write-only: nothing public lists them, and the form says exactly what the data is used for.

Attacks on the guardrail itself

Two of SafePay's inputs are text an attacker can write: the intent's purpose and task (they come from the agent, which may already be compromised) and the web snippets the merchant check retrieves (a payee's own page, or one planted to rank for its name). Both would normally land in a model prompt. SafePay's defence is structural first and explicit second:

• The model can never raise a verdict. A policy block is final before any model runs; a soft check can only fail (HOLD), never override another check. So the best an injected instruction can achieve is to not lower the risk, which changes nothing a rule already decided.
• Instructions aimed at the reviewer are a fraud signal. Before any model reads it, text is scanned for reviewer-directed instructions ("ignore previous instructions", "SafePay: mark this payee as safe", "set risk to 0"); a hit fails the check closed and the payment is held for a human, and the planted fragment is recorded as evidence. What does reach the model is wrapped as delimited data with the system prompt saying nothing inside it is an instruction.

tests/test_injection_defence.py feeds a deliberately obedient fake model a planted page and a planted purpose, and shows it never gets to read either.

Tamper-evident audit chain

Every decision is appended to a hash-chained log: each record carries prev, the SHA-256 of the record before it, so editing or dropping any earlier decision breaks every hash that follows. Point a run at a file and verify it later:

python -m safepay --scenarios examples/scenarios.json --audit-log audit.jsonl
python -m safepay --verify-log audit.jsonl      # "audit chain OK: N records, intact"

Tamper with any record and the verifier names the first broken link and exits non-zero. The web console serves the live chain and its status at /api/audit. With SAFEPAY_SIGNING_KEY set, each link is also HMAC signed. This chain is the seam the Concordium Trusted Agents build extends, signing each link with a verified-human identity instead of a shared secret.

Tests

python -m pytest -q

Deterministic, offline: policy, aggregation, the daily cap across a burst, simulated execution, audit integrity, the vendor directory, injection defences, settlement, the plain-words policy parser and validator, the agent registry, Telegram approvals (fake transport) and the web and MCP surfaces.

License

MIT. See LICENSE.