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Indirect prompt injection defense for AI agents using tool calls (MCP, CLI, or direct APIs). Detects and neutralizes attacks hidden in tool results (emails, documents, PRs, etc.) before they reach your LLM.

Python package: stackone-defender — aligned with @stackone/defender on npm.

Installation

pip

pip install stackone-defender

uv

uv add stackone-defender

Tier 2 (ONNX) — add extras:

pip install stackone-defender[onnx]
# or: uv add "stackone-defender[onnx]"

The ONNX model (~22MB) is bundled in the wheel — no extra downloads at runtime.

SFE preprocessor (optional) — add extras:

pip install stackone-defender[sfe]
# or: uv add "stackone-defender[sfe]"

The [sfe] extra installs fasttext-ng (provides the fasttext module). It requires NumPy 2.3+. PyPI may ship a wheel only for some platforms; otherwise pip/uv builds from source (needs a C++ toolchain).

Quick start

from stackone_defender import create_prompt_defense

# Tier 1 + Tier 2 are on by default. block_high_risk=True enables allow/block.
defense = create_prompt_defense(block_high_risk=True)

# Optional: preload ONNX to avoid first-call latency (requires [onnx] extra)
defense.warmup_tier2()

result = defense.defend_tool_result(tool_output, "gmail_get_message")

if not result.allowed:
    print(f"Blocked: risk={result.risk_level}, score={result.tier2_score}")
    print(f"Detections: {', '.join(result.detections)}")
else:
    send_to_llm(result.sanitized)

How it works

defend_tool_result() runs two tiers:

Tier 1 — Pattern detection (sync, ~1 ms)

• Unicode normalization — homoglyph resistance (e.g. Cyrillic а → ASCII a)
• Role stripping — SYSTEM:, ASSISTANT:, <system>, [INST], etc.
• Pattern removal — phrases like “ignore previous instructions”
• Encoding detection — suspicious Base64/URL-shaped payloads
• Boundary annotation (opt-in) — [UD-{id}]…[/UD-{id}] wrappers when annotate_boundary=True (npm: annotateBoundary). Use generate_boundary_instructions from the package root in prompts when you enable wrapping.

Tier 2 — ML classification (ONNX)

Packed-chunk MiniLM classifier (int8 ONNX ~22 MB, bundled):

• Split text into sentences, pack to model-sized chunks, score chunks in batched ONNX calls
• Catches paraphrased or novel injections missed by regex
• Uses chunked batch inference to bound memory on large payloads

Optional SFE preprocessor

• use_sfe=True runs a field-level FastText pass to build a classifier-only view of the payload
• Tier 1 always sanitizes the original tool value; sanitized in DefenseResult is unchanged by SFE drops
• Tier 2 extracts strings from the SFE-filtered tree; fields_dropped lists paths omitted from that extraction (not removed from sanitized)
• Fails open if the runtime/model is unavailable: payload continues unfiltered

Benchmarks (F1 @ threshold 0.5):

Benchmark	F1	Samples
Qualifire (in-distribution)	0.8686	~1.5k
xxz224 (out-of-distribution)	0.8834	~22.5k
jayavibhav (adversarial)	0.9717	~1k
Average	0.9079	~25k

Optional Tier 3 — LLM adjudication (consumer-supplied)

Authoritative LLM-based classification for the cases Tier 2 finds ambiguous. The package ships only the orchestration and the Tier3Provider interface — the actual model endpoint (a hosted LLM, OpenAI, an internal inference service) lives in your code, keeping proprietary models and credentials out of the package.

Two modes, selected via defender_mode:

• "cascade" (default): Tier 1 → Tier 2 → Tier 3, with Tier 3 invoked only when the Tier 2 effective score falls in the gray band (default [0.3, 0.85)). The Tier 3 verdict overrides Tier 2 on the escalated chunk — a block forces a block, an allow rescues it. Outside the band defender skips the round trip.
• "tier3_only": skip Tier 2; the block/allow decision is the Tier 3 verdict alone. Tier 1 still sanitizes the returned sanitized payload.

Register a provider once at startup, then opt in per instance:

from typing import Any

from stackone_defender import create_prompt_defense, set_default_tier3_provider
from stackone_defender.types import Tier3Verdict

class MyProvider:
    def classify(self, text: str, *, ctx: dict[str, Any] | None = None) -> Tier3Verdict:
        # Call your LLM endpoint here (sync or awaitable). ctx["toolName"] is available.
        verdict = call_my_llm(text, tool_name=(ctx or {}).get("toolName"))
        return Tier3Verdict(decision="block" if verdict.block else "allow", score=verdict.p_block)

set_default_tier3_provider(MyProvider())

defense = create_prompt_defense(
    block_high_risk=True,
    enable_tier3=True,
    defender_mode="cascade",  # or "tier3_only"
    tier3={
        "provider": MyProvider(),                          # optional: overrides the registry for this instance
        "escalation_band": {"lower": 0.3, "upper": 0.85},  # cascade gray band; [lower, upper), defaults shown
        "max_text_length": 10000,                          # caps text passed to the provider
        "block_threshold": 0.622,                          # optional; decide on score, not the model's word
    },
)

Choosing the operating point (block_threshold). By default the model's decision word is authoritative — but that word is its argmax, an implicit 0.5 cut that moves on its own whenever the model is retrained. Set tier3.block_threshold to decide on verdict.score (P(block)) instead: the cut becomes an explicit config value — raise it to trade recall for fewer false positives, lower it for the reverse; 0.5 reproduces argmax. It requires a provider that reports score as P(block) (not "confidence in whichever decision I made" — those invert on allows). If score is missing or outside [0, 1] the verdict's decision is used instead and defender warns once, so a provider that cannot report a score degrades to the default rather than failing.

Fail-open semantics. A provider error or timeout records a skip_reason on result.tier3; in cascade defender falls back to the Tier 2 decision, in tier3_only it allows the request. enable_tier3=True with no registered provider falls back to the standard Tier 1 + Tier 2 cascade and logs one warning per instance — Tier 3 misconfiguration never silently disables defense. result.tier3 carries the verdict (a Tier3Verdict, or a Tier3Skip when the provider ran but returned nothing usable) when Tier 3 runs, and is None when it doesn't.

allowed vs risk_level

• Use allowed for gating when block_high_risk=True: False means do not pass sanitized to the model as-is.
• risk_level is diagnostic: it starts at default_risk_level (default "medium") and is escalated by Tier 1 / Tier 2 signals — not reduced. Use it for logging, not as the sole block signal unless you implement your own policy.

Level	Typical trigger
low	No strong signals
medium	Lighter pattern / sanitization signals
high / critical	Strong injection patterns, encoding signals, or high Tier 2 score

API

create_prompt_defense(**kwargs)

defense = create_prompt_defense(
    enable_tier1=True,
    enable_tier2=True,
    block_high_risk=False,
    default_risk_level="medium",
    annotate_boundary=False,  # True: wrap risky strings with [UD-…] tags (npm: annotateBoundary)
    tier2_fields=["subject", "body", "snippet"],  # optional: scope Tier 2 to these JSON keys (default: all strings)
    use_sfe=True,  # optional: enable semantic field extractor preprocessing
    config={
        "tier2": {
            "high_risk_threshold": 0.8,
            "tier2_fields": None,  # or list[str]; constructor tier2_fields wins if set
        },
    },
)

defense.defend_tool_result(value, tool_name)

Runs Tier 1 sanitization on risky fields of the original payload, then Tier 2 on strings from the SFE-filtered view when SFE is on (otherwise the full value). Optional tier2_fields restricts Tier 2 extraction to specific keys; omit it to classify all strings (matches @stackone/defender 0.6.3). Synchronous — no await.

from dataclasses import dataclass, field

@dataclass
class DefenseResult:
    allowed: bool
    risk_level: RiskLevel
    sanitized: Any
    detections: list[str]
    fields_sanitized: list[str]
    patterns_by_field: dict[str, list[str]]
    tier2_score: float | None = None
    tier2_skip_reason: str | None = None
    max_sentence: str | None = None
    fields_dropped: list[str] = field(default_factory=list)
    truncated_at_depth: bool | None = None
    latency_ms: float = 0.0

defense.defend_tool_results(items)

Sync batch API. When enable_tier3=True, uses one asyncio.run() and defends items concurrently via asyncio.gather (same scheduling model as npm defendToolResults; blocking sync providers still run one at a time on the event-loop thread). From async code, prefer defend_tool_results_async.

results = defense.defend_tool_results([
    {"value": email_data, "tool_name": "gmail_get_message"},
    {"value": doc_data, "tool_name": "documents_get"},
    {"value": pr_data, "tool_name": "github_get_pull_request"},
])
for r in results:
    if not r.allowed:
        print("Blocked:", ", ".join(r.fields_sanitized))

await defense.defend_tool_results_async(items)

Async batch API — runs defend_tool_result_async per item concurrently via asyncio.gather. Required when Tier 3 is enabled inside a running event loop (e.g. FastAPI).

results = await defense.defend_tool_results_async([
    {"value": email_data, "tool_name": "gmail_get_message"},
    {"value": doc_data, "tool_name": "documents_get"},
])

defense.analyze(text)

Tier 1 only — useful for debugging pattern hits without full tool-result traversal.

Tier 2 warmup

defense = create_prompt_defense()
defense.warmup_tier2()  # no-op if enable_tier2=False or ONNX extra missing

Integration example

from stackone_defender import create_prompt_defense

defense = create_prompt_defense(block_high_risk=True)
defense.warmup_tier2()

def run_tool_and_defend(raw_result: dict, tool_name: str):
    outcome = defense.defend_tool_result(raw_result, tool_name)
    if not outcome.allowed:
        return {"error": "Content blocked by safety filter", "risk_level": outcome.risk_level}
    return outcome.sanitized

# Example agent loop
sanitized = run_tool_and_defend(gmail_api.get_message(msg_id), "gmail_get_message")

Risky field detection

Only string values under configured “risky” keys are scanned and sanitized. RiskyFieldConfig provides global names/patterns plus tool_overrides (wildcard tool names → field list), same idea as the npm package.

Tool pattern	Scanned fields
gmail_*, email_*	subject, body, snippet, content
documents_*	name, description, content, title
github_*	name, title, body, description, message
hris_*	name, notes, bio, description
ats_*	name, notes, description, summary
crm_*	name, description, notes, content

Otherwise the default list applies: name, description, content, title, notes, summary, bio, body, text, message, comment, subject, plus suffix patterns like *_body, *_description, etc. Structural keys such as id, url, created_at are not treated as risky by default.

Development

uv sync --group dev
uv run pytest

License

Apache-2.0 — see LICENSE.