unplug-ai 0.3.0

Pull the plug on bad AI. Fast prompt injection detection and redaction for LLM apps, agents, and RAG pipelines.

Unplug SDK

Find the attack. Cut the attack. Keep the rest.

Unplug is a runtime defense layer for LLM apps and agents. It tracks where every piece of text came from, scans untrusted content for prompt injection, and gates tool calls before they do damage. Attacks are redacted at the span level, so the rest of the document stays usable.

Why Unplug

• Span-level redaction. Binary blocking throws away the whole document. Unplug localizes the injected instruction to character offsets and removes just that.
• Provenance built in. Nothing enters as a raw string. Every text carries its source (user, retrieved, tool output) and trust level.
• Tool-call gates. Destructive calls block. Tainted sessions force review before side-effect tools run.
• Fail closed. Scanner errors block, never silently allow.
• Offline by default. Regex + normalization scanning needs zero ML dependencies. One line upgrades to the ML span model.

Install

pip install unplug-ai           # regex-only core, zero ML deps
pip install "unplug-ai[ml]"     # add the ML span model

Or from source:

git clone https://github.com/UnplugAI/Unplug.git && cd Unplug/sdk
uv sync && uv pip install -e ".[ml]"

60-second quickstart

from unplug import Guard

guard = Guard()  # local mode, offline, regex scanners

result = guard.scan("Ignore all previous instructions", source="user")
if not result.safe:
    print(result.action)          # block / review / redact
    print(result.redacted_text)   # attack spans replaced
    print(result.findings)        # evidence with span offsets

Upgrade to the ML span model. Weights download once from Unplug-AI/unplug-tiny-v1 and cache locally:

guard = Guard.with_tiny()
result = guard.scan(rag_chunk, source="retrieved")

No install needed to try it: live demo on Hugging Face.

Protect an agent

Wire Unplug into any agent that fetches external content or calls tools:

1. Scan user input. guard.scan(text, source="user") captures user_intent for later gates.
2. Wrap untrusted content before it enters LLM context. guard.wrap_for_context(rag_chunk, source="retrieved"). Auto-wrap also runs on scan(..., source="retrieved") when [boundaries] auto_wrap_untrusted = true.
3. After fetch/read tools. guard.notify_taint_source("web_fetch") so side-effect tools require review.
4. Before every tool call. guard.check_tool_call(name, args, taint_sources=[...]). Destructive calls block. A tainted session plus a side-effect tool returns REVIEW. Crescendo patterns tighten exec, web_fetch, and browser tools adaptively ([degradation]).
5. Scan agent output. guard.scan_output(text). Set strip_on_output = true to remove boundary markers from redacted output.
6. New trusted turn. guard.reset_session_taint() clears taint and degradation.

Context files (AGENTS.md and similar): guard.scan_context_file(text, filename="AGENTS.md") before loading into the system prompt.

Full walkthrough: examples/agent_exfil_demo.py shows a hidden webpage injection leading to a tainted session and a blocked exfil tool call.

Long documents and streams

Documents past 8K chars are scanned with sliding windows (2048 chars, 256 overlap) so the full text is covered, not just head and tail. Configure under [catalog.tiers.tiny.config] or unplug.toml.

# Streamed LLM output: scan incrementally, full coverage on flush
scanner = guard.stream_scanner(scan_every_chars=1024)
for chunk in token_stream:
    if hit := scanner.push(chunk):
        handle(hit)
result = scanner.flush()

# Or scan a finished chunk list as one document
guard.scan_stream(["part1", "part2", "part3"])

Deployment modes

Mode	When to use	Init	ML runs where
Local regex	Dev, air-gapped, zero deps	Guard()	Nowhere
Local + ML	Single agent, offline	Guard.with_tiny() or active_model="tiny"	Agent process
Hosted	Production, no GPU on client	Guard(mode="server") + API key	Unplug API
Local sidecar	Many local agents, one model load	Sidecar + Guard(mode="server") to localhost	Local server

Full architecture and decision guide: docs/DEPLOYMENT.md.

Hosted

export UNPLUG_SERVER_URL=https://api.your-unplug-host.com
export UNPLUG_API_KEY=up_live_xxxxxxxx

guard = Guard(mode="server")  # or server_url= / server_api_key= in ctor

The server handles /v1/scan and /v1/scan/output. check_tool_call() always runs locally (toolchain, collusion, taint). See examples/hosted_client.py.

Local sidecar

Same wire format as hosted, run on localhost without an API key:

# Terminal 1, from the unplug-server repo
docker compose -f docker-compose.sidecar.yml up

# Terminal 2
export UNPLUG_SERVER_URL=http://127.0.0.1:8000
unplug-sidecar doctor
python examples/local_sidecar_client.py

ML model: unplug-tiny

The dual-head checkpoint has a document classifier (recall) and a BIOES span head (localization and redaction). Without it, regex + tool enforcement remain the default.

pip install "unplug-ai[ml]"
unplug-models download tiny   # optional; Guard.with_tiny() auto-downloads too

# unplug.toml
active_model = "tiny"
auto_download_model = true
require_ml = true   # optional fail-fast at init

UNPLUG_MODEL_PATH alone auto-selects the tiny tier; prefer setting both explicitly in production. Checkpoint layout and integration steps: docs/ML_INTEGRATION.md.

All published model metrics come from a frozen golden-eval harness on held-out data and are recorded on the model card. No hand-typed numbers, measured not target.

Verify your wiring anytime:

unplug-audit                   # wiring + ML status
unplug-audit --probes          # FP + encoding + boundary batteries
unplug-audit --require-ml      # fail if checkpoint / config / ML not active

Check	Meaning
ml_checkpoint	Checkpoint dir found on disk
ml_configured	active_model set in config
ml_active	injection_ml loaded and weights ready

Configuration

Copy unplug.example.toml to unplug.toml to customize scanners, tool profiles, boundaries, and limits.

Variable	Hosted	Local ML
UNPLUG_SERVER_URL	required	-
UNPLUG_API_KEY	required if server auth on	-
UNPLUG_ACTIVE_MODEL	-	tiny
UNPLUG_MODEL_PATH	-	checkpoint dir
UNPLUG_REQUIRE_ML	-	optional

Integrations

Framework hooks for LangGraph and Agno, plus framework-agnostic patterns: docs/INTEGRATIONS.md.

Threat scanners live under unplug.safeguards (canonical). The older unplug.scanners path still works but emits deprecation warnings:

from unplug.safeguards.injection import InjectionScanner
from unplug.safeguards.destructive import DestructiveScanner
from unplug.safeguards.registry import SafeguardRegistry

Examples

• examples/agent_exfil_demo.py: hidden injection, tainted session, blocked exfil tool call
• examples/langgraph_hooks_demo.py and examples/agno_hooks_demo.py: framework hooks
• examples/hosted_client.py and examples/local_sidecar_client.py: server modes
• demo/: the Gradio app behind the Hugging Face demo

Documentation

Doc	Covers
docs/DEPLOYMENT.md	Hosted vs embedded vs sidecar architecture
docs/BENCHMARKS.md	Regex SDK eval results (neuralchemy, microsoft)
docs/ML_INTEGRATION.md	Checkpoint layout, thresholds, long-text and streaming config
docs/INTEGRATIONS.md	LangGraph, Agno, framework-agnostic hooks
docs/AGENT_FLOW_SECURITY.md	End-to-end agent hardening flow
docs/HERMES_AGENT_SECURITY.md	Context-file scanning for agent frameworks

Development

cd sdk && uv sync --all-extras --dev

make fix          # auto-fix lint + format
make check        # lint + format check + full pytest
make check-ci     # CI parity: check + exfil demo + security regression
make test-security
make audit        # unplug-audit wiring
make audit-ml     # unplug-audit --require-ml

Contributions welcome. See CONTRIBUTING.md.

License

Apache-2.0