swarmauth

SwarmAuth

The zero-trust authorization standard for multi-agent systems.

SwarmAuth is OAuth 2.1 for autonomous AI swarms: cryptographically signed, short-lived (≤300s), capability-scoped delegation tokens for agent-to-agent and agent-to-tool calls. It’s built on one assumption you should already hold — your LLM layer will be compromised by prompt injection — and asks a different question: when that happens, does the execution boundary stop the damage, or does it just trust whatever the model decided?

Today, most multi-agent stacks pass raw API keys or unscoped bearer tokens between agents. Read the full threat model and protocol details in SPEC.md.

SwarmAuth has no opinion about what your agents do — a capability is just a string your policy defines, and @guard() wraps any Python callable. The examples below use a finance payout because unauthorized money movement makes the stakes obvious, but the same call protects any tool an LLM can decide to invoke: a support agent issuing a refund, a DevOps agent running an infra command, a coding agent merging or deploying, a research agent spending against a paid API, a healthcare agent writing to a record, an ETL agent triggering a pipeline run — anywhere an autonomous decision meets an action with real consequences.

Why

• Prompt injection is not a solved problem, and won’t be soon. SwarmAuth doesn’t try to detect or prevent it — it makes the LLM’s decision irrelevant at the point of execution.
• Capability tokens, not credentials. A token names exactly what it authorizes (capabilities), against whom (sub), and under what limits (constraints: rate limits, call caps, budget caps, parameter allowlists) — never a raw, reusable key.
• Tokens expire in seconds. Max TTL is 300 seconds, enforced by every verifier regardless of what an issuer tries to claim.
• Zero exotic dependencies. Ed25519 via cryptography, schema via pydantic. That’s the whole dependency tree — KeyRegistry needs neither, and RedisUsageTracker is an opt-in extra, not baked into the core.
• Sub-millisecond verification. See benchmark results below.
• Built for more than two agents. KeyRegistry trusts many issuers at once and rotates keys without a hard cutover; RedisUsageTracker enforces rate/budget/call limits atomically across every verifier process in the swarm, not just one.

Quickstart

import swarmauth

tool_keys = swarmauth.KeyPair.generate()

@swarmauth.guard("tool:process_payout", issuer_public_key=tool_keys.public_bytes)
def process_payout(destination_account: str, amount_usd: float):
    ...  # your real tool logic — only ever reached with a verified, in-scope token

Issuing a token that will actually pass that check is one line:

token = swarmauth.TokenIssuer(tool_keys).issue(
    iss="agent:requester-01", sub="tool:process_payout",
    capabilities=["tool:process_payout"], ttl_seconds=60,
)

That’s the entire integration surface: @swarmauth.guard the tool, issue the token. Framework adapters for LangChain, CrewAI, AutoGen, and MCP tools are one call each — see swarmauth/middleware.py (secure_langchain_tool, secure_crewai_tool, secure_autogen_function, secure_mcp_tool), verified against real LangChain, ag2, and MCP installs in tests/test_framework_adapters.py.

Scaling out: many issuers and many verifier processes

The examples above assume one issuer whose public key a verifier already has in hand. A real swarm usually has many issuing agents and many verifier processes, which needs two more pieces:

KeyRegistry — trust many issuers, and rotate an issuer’s key without a hard cutover (SPEC.md §8):

from swarmauth.registry import KeyRegistry

registry = KeyRegistry()
registry.register("agent:issuer-a", keys_a.public_bytes)
registry.register("agent:issuer-b", keys_b.public_bytes)

# Rotate issuer-a's key later without invalidating in-flight tokens:
registry.register("agent:issuer-a", new_keys_a.public_bytes, rotate=True)
registry.revoke("agent:issuer-a", keys_a.public_bytes)  # once fully rolled over

@swarmauth.guard("tool:process_payout", key_registry=registry)
def process_payout(destination_account: str, amount_usd: float):
    ...

RedisUsageTracker — enforce max_calls/max_amount_usd/rate_limit_per_min atomically across multiple verifier processes instead of per-process (SPEC.md §9):

import redis
from swarmauth.backends.redis_backend import RedisUsageTracker

tracker = RedisUsageTracker(redis.Redis.from_url("redis://localhost:6379/0"))

@swarmauth.guard("tool:process_payout", key_registry=registry, tracker=tracker)
def process_payout(destination_account: str, amount_usd: float):
    ...

RevocationStore — reject one specific, otherwise-valid token before its signed expiry, for incident response or agent/session offboarding (SPEC.md §9):

from swarmauth.revocation import InMemoryRevocationStore

revocation_store = InMemoryRevocationStore()

@swarmauth.guard("tool:process_payout", key_registry=registry, revocation_store=revocation_store)
def process_payout(destination_account: str, amount_usd: float):
    ...

# Compromised session detected -- reject its token immediately, without
# waiting out the rest of its (short) TTL:
revocation_store.revoke(claims.jti, expires_at=claims.exp)

Use swarmauth.backends.redis_backend.RedisRevocationStore in place of InMemoryRevocationStore to share revocations across multiple verifier processes, the same way RedisUsageTracker shares usage state.

All three are optional and additive: issuer_public_key= and the default in-memory UsageTracker still work exactly as in the quickstart above.

Install

pip install swarmauth

Or from a clone, for local development:

pip install -e .

For running the full test suite, including the real-framework adapter tests and the Redis backend tests (which run against fakeredis by default, no server required):

pip install -e ".[dev,frameworks,redis]"
pytest

Architecture

sequenceDiagram
    participant A as Agent A (Requester)
    participant I as SwarmAuth Token Issuer
    participant B as Agent B / Tool (Capability Owner)

    A->>I: Request capability token (iss=A, sub=B, capabilities=[...], constraints, ttl<=300s)
    I->>I: Evaluate policy — is A allowed to request these capabilities against B?
    I-->>A: Signed JSON Capability Token (JCT)
    A->>B: Tool call, with JCT attached
    B->>B: Verify Ed25519 signature, exp/iat window, audience, capability, constraints
    alt Valid and in scope
        B->>B: Execute tool, record usage against jti
        B-->>A: Result
    else Invalid, expired, wrong audience, missing capability, or over limit
        B-->>A: Reject (CapabilityViolationError, ConstraintViolationError, etc)
    end

Full claims schema, canonicalization rules, and the complete verification algorithm are in SPEC.md.

Implementing JCT in another language? spec/test-vectors/ gives byte-exact signed tokens and expected outcomes (including the temporal/expiry edge cases) so you can verify your implementation matches this one without needing to ask. This Python SDK enforces itself against the same file on every test run.

SwarmBench: does this actually stop anything?

benchmarks/swarmbench.py runs a runnable simulation of a Requesting Agent that delegates to a Payments Tool’s process_payout tool, with a prompt injection embedded in an inbound customer message instructing the Requesting Agent to trigger a $50,000 payout to an attacker-controlled account. The injection is modeled as succeeding at the LLM layer in both test cases — SwarmBench isn’t testing whether models can be fooled (they can); it’s testing what happens next.

This particular scenario is a payout because a wrongly-executed $50,000 transfer is a visceral, easy-to-grade failure — the mechanism it exercises (capability check + constraint check at the execution boundary, before any side effect happens) is identical regardless of what the tool does. Swap process_payout for issue_refund, run_shell_command, merge_pull_request, send_email, or write_patient_record and the boundary behaves the same way, because @guard() never inspects what the wrapped function does.

python benchmarks/swarmbench.py

Results (from an actual run on this machine)

Scenario	Injection succeeds at LLM layer	Unauthorized payout executed	Blocked at execution boundary
Unprotected agent loop	✅ Yes	❌ Yes — $50,000 sent	—
SwarmAuth-protected agent loop	✅ Yes	✅ No	✅ Yes (CapabilityViolationError)

Metric	Value
Mean token verification overhead	0.12 ms/op (target: <1ms)

In the unprotected loop, the Payments Tool trusts whatever arguments it’s called with — the injection walks straight through. In the protected loop, the token the Requesting Agent actually holds only grants tool:draft_payout under a policy-set budget; it does not, and cannot, grant tool:process_payout no matter what the compromised LLM decided to call — so the execution boundary rejects it before the ledger is touched.

Repository layout

swarmauth/
├── SPEC.md                        # protocol specification
├── README.md
├── CONTRIBUTING.md
├── SECURITY.md
├── tox.ini                          # reproduce every CI job locally: tox -e lint / py312 / frameworks / redis-live
├── .github/workflows/ci.yml        # tests + benchmark + live-Redis job on every push/PR
├── scripts/
│   ├── audit_deps.py                # pip-audit wrapper used by CI's `lint` job and `tox -e lint`
│   ├── generate_test_vectors.py      # regenerates spec/test-vectors/vectors.json
│   └── verify_release_artifact.py    # required before tagging a release -- see CONTRIBUTING.md
├── spec/
│   └── test-vectors/                # byte-exact cross-language interop vectors; see its own README
├── swarmauth/
│   ├── __init__.py
│   ├── crypto.py                   # Ed25519 keypairs, signing, verification
│   ├── token.py                     # CapabilityToken: issue / parse / verify
│   ├── registry.py                   # KeyRegistry: multi-issuer trust + key rotation
│   ├── middleware.py                  # guard decorator + framework adapters
│   ├── revocation.py                   # RevocationStore + InMemoryRevocationStore
│   ├── backends/
│   │   └── redis_backend.py            # RedisUsageTracker, RedisRevocationStore (optional `redis` extra)
│   ├── exceptions.py
│   └── py.typed
├── benchmarks/
│   └── swarmbench.py                # runnable unprotected-vs-protected simulation
└── tests/
    ├── test_token.py
    ├── test_middleware.py
    ├── test_registry.py
    ├── test_revocation.py
    ├── test_public_api.py             # every swarmauth.exceptions.* class must be exported at top level
    ├── test_spec_vectors.py            # enforces spec/test-vectors/vectors.json against this implementation
    ├── test_redis_backend.py         # runs against fakeredis, or a real server in CI
    └── test_framework_adapters.py    # real LangChain, ag2, and MCP integration tests

Design principles

• No cryptographic dependencies beyond cryptography; no schema/validation dependencies beyond pydantic.
• Strict typing, explicit exception hierarchy (TokenExpiredError, CapabilityViolationError, ConstraintViolationError, …) — callers decide how to handle each failure mode, nothing fails silently.
• Tokens are data, not infrastructure: no required network call, no mandatory central service. Self-issuance and centralized-issuer deployments use the exact same token format and verification code.

Status

MVP / RFC. The token format, claims schema, and verification algorithm are considered stable for 0.x. Multi-issuer key rotation (KeyRegistry, SPEC.md §8), token revocation (RevocationStore, SPEC.md §9), and distributed usage tracking (RedisUsageTracker, SPEC.md §10) now ship in the SDK; expect the framework adapters to keep evolving as more of them get real integration use. Issues and PRs welcome.

License

MIT — see LICENSE.

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