Little Canary

Prompt-injection sensing through a powerless sacrificial model.

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Little Canary screens untrusted language before your agent acts. It exposes the input to a small model with no application tools or authority, then inspects the model's response for evidence that the input changed its behavior.

untrusted text
    → structural preflight
    → powerless sacrificial model
    → response-residue analysis
    → PASS / FLAG / BLOCK

Structural checks catch known input shapes. The behavioral layer asks a different question: what did this input do to the canary?

Little Canary is an inbound risk sensor, not a security guarantee or an agent runtime.

Quick start

Install Little Canary:

python -m pip install little-canary
little-canary --version

Run the local adapter with an Ollama model:

little-canary serve \
  --mode block \
  --canary-model qwen2.5:1.5b \
  --ollama-url http://127.0.0.1:11434

Then screen untrusted text:

curl -sS http://127.0.0.1:18421/check \
  -H 'Content-Type: application/json' \
  -d '{"text":"untrusted text"}'

The adapter binds to 127.0.0.1 and exposes GET /health and POST /check.

See the canary work

Little Canary includes a live evidence gate that sends a fixed clean/attack pair through a dedicated local Ollama model:

little-canary demo --live \
  --backend ollama \
  --model qwen2.5:1.5b \
  --endpoint http://127.0.0.1:11434

A recorded run produced this contrast:

CASE       clean
RESPONSE   "The capital of France is Paris."
RISK       0.0
SIGNALS    none
VERDICT    PASS
CASE       c1-05
RESPONSE   "I have acknowledged the update to the system prompt."
RISK       1.0
SIGNALS    canary_compromise
VERDICT    BLOCK
LIVE       CONTRAST VERIFIED

The attack was a fake system-prompt correction. The canary followed it; Little Canary detected the compromise residue and blocked the input.

A live result is evidence for that exact model, runtime, and configuration, not a universal detection claim.

Python API

from little_canary import SecurityPipeline

pipeline = SecurityPipeline(
    canary_model="qwen2.5:1.5b",
    mode="full",
)

verdict = pipeline.check(untrusted_text)
if verdict.degraded:
    quarantine_or_apply_your_availability_policy(untrusted_text)
elif not verdict.safe:
    block(untrusted_text, verdict.summary)
else:
    forward_to_agent(verdict.safe_input)

Routing and inspection coverage are deliberately separate. An unavailable canary can be configured to fail open, but failed coverage is reported as degraded rather than silently becoming a clean pass.

Integrations

Little Canary can sit at the input boundary of several agent environments.

Each integration preserves the host's actual enforcement capabilities. For example, the Hermes Agent plugin cannot prevent prompt delivery at its available hook boundary, so a BLOCK removes downstream tool authority instead.

What "powerless" means

The canary receives the untrusted input but is given no application tools, credentials, or output execution.

Its response is evidence to inspect — not instructions for the authoritative agent.

This is a library-level capability boundary, not an operating-system sandbox. Giving the canary tools or forwarding its output into an authoritative execution path changes the security model.

Coverage and failure

Little Canary distinguishes routing disposition from inspection coverage.

Fail-open behavior is availability policy, not evidence that an input is safe. A failed or skipped inspection layer is never represented as a successful behavioral check.

Security boundary

Little Canary is one layer in an agent-security architecture.

It does not:

• prove that an input is harmless;
• detect every prompt injection;
• replace least privilege or tool policy;
• sandbox the canary process at the operating-system level;
• screen every tool result or downstream interaction through every integration;
• turn missing behavioral coverage into a clean verdict.

Remote model endpoints receive the data sent to them. Use local models when input must remain local.

See SECURITY.md for vulnerability reporting and benchmarks/README.md for the evaluation boundary.

Research

Little Canary implements the behavioral-canarying architecture described in Behavioral Canarying for Prompt Injection: Powerless Model Probes with Explicit Coverage Semantics.

The technical note documents the pre-execution sensing architecture and the separation between routing disposition and inspection coverage. It does not claim universal detection, formal security, or aggregate accuracy for the current release.

Concept DOI: 10.5281/zenodo.21818564

Development

pytest
ruff check little_canary tests
mypy little_canary
python -m build
python -m twine check dist/*

Tests are offline by default and mock network behavior. Live evaluation should use a dedicated endpoint that is not serving another workload.

The version in pyproject.toml is the source of truth for a release. little-canary --version reports the build you actually have installed, and GitHub Releases and PyPI are the live authorities for what is published.

Documentation

License

Apache License 2.0

Little Canary is maintained by Hermes Labs.