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idpishield

idpishield is a Go library for detecting indirect prompt injection (IDPI) risk in untrusted text before it is passed to an LLM.

It provides a single core assessment engine and two adapters around it:

  • Go API (primary)
  • CLI and MCP server (secondary interfaces)

Why Use It

Use this library when your system ingests untrusted content (web pages, user text, scraped HTML, documents) and you want a fast risk signal before forwarding content into an LLM prompt.

Core output includes:

  • score (0-100)
  • level (safe, low, medium, high, critical)
  • blocked (policy decision based on score + strict mode)
  • matched patterns and categories

Install

Go Library

go get github.com/pinchtab/idpishield

CLI / MCP Server

Homebrew:

brew install pinchtab/tap/idpishield

curl:

curl -fsSL https://raw.githubusercontent.com/pinchtab/idpishield/main/install.sh | bash

Go install:

go install github.com/pinchtab/idpishield/cmd/idpishield@latest

Import

import idpi "github.com/pinchtab/idpishield"

Minimal Usage

package main

import (
	"fmt"
	"log"

	idpi "github.com/pinchtab/idpishield"
)

func main() {
	shield, err := idpi.New(idpi.Config{Mode: idpi.ModeBalanced})
	if err != nil {
		log.Fatal(err)
	}

	result := shield.Assess("Ignore all previous instructions", "https://example.com")
	fmt.Printf("score=%d level=%s blocked=%v\n", result.Score, result.Level, result.Blocked)
}

Configuration

cfg := idpi.Config{
	Mode:           idpi.ModeBalanced,
	AllowedDomains: []string{"example.com", "google.com"},
	StrictMode:     false,
	ServiceURL:     "", // optional for deep-mode service augmentation
	ServiceTimeout: 0,
	ServiceRetries: 0,
	ServiceCircuitFailureThreshold: 0,
	ServiceCircuitCooldown: 0,
	MaxInputBytes: 0,
	MaxDecodeDepth: 0,
	MaxDecodedVariants: 0,
}

Modes

  • fast: lightweight pattern checks
  • balanced: recommended default for most integrations
  • deep: includes deep-mode path (optionally with service)

Domain Handling

  • AllowedDomains is optional.
  • If set, assessments can incorporate allowlist domain decisions when a URL is provided to Assess(text, url).

Blocking Semantics

  • default mode blocks at score >= 60
  • strict mode blocks at score >= 40

Resilience And Performance Controls

  • MaxInputBytes: caps analyzed text size (0 means unlimited).
  • MaxDecodeDepth: limits recursive decoding depth for obfuscated payloads.
  • MaxDecodedVariants: limits the number of decoded variants scanned.
  • ServiceRetries: retries transient deep-service failures (for deep mode).
  • ServiceCircuitFailureThreshold + ServiceCircuitCooldown: opens a temporary circuit when deep service repeatedly fails, keeping local detection responsive.

Result Semantics

RiskResult is the main output contract:

type RiskResult struct {
	Score      int
	Level      string
	Blocked    bool
	Reason     string
	Patterns   []string
	Categories []string
}

Interpretation guide:

  • score is the numeric risk estimate.
  • level is a severity bucket derived from score.
  • blocked is a policy output (score + strict mode), not just a detection flag.
  • reason, patterns, categories provide explainability for audit/logging.

Public API (Go)

Canonical assessment method:

  • Assess(text, url)

Primary exported surface:

type Config struct {
	Mode           Mode
	AllowedDomains []string
	StrictMode     bool
	ServiceURL     string
	ServiceTimeout time.Duration
	ServiceRetries int
	ServiceCircuitFailureThreshold int
	ServiceCircuitCooldown time.Duration
	MaxInputBytes int
	MaxDecodeDepth int
	MaxDecodedVariants int
}

type Mode string

const (
	ModeFast     Mode = "fast"
	ModeBalanced Mode = "balanced"
	ModeDeep     Mode = "deep"
)

func New(cfg Config) (*Shield, error)
func (s *Shield) Assess(text, url string) RiskResult
func (s *Shield) Wrap(text, url string) string
func (s *Shield) InjectCanary(prompt string) (injectedPrompt, token string, err error)
func (s *Shield) CheckCanary(response, token string) CanaryResult

Wrap is useful when you want to preserve data while adding trust-boundary markers before sending content into prompts.

InjectCanary and CheckCanary implement canary token detection for prompt leakage (see below).

Canary Tokens

Canary tokens help detect when an LLM may have leaked or echoed hidden prompt content — a potential signal of goal hijacking or prompt extraction, though not definitive proof.

Usage

// Before calling the LLM, inject a canary token
augmented, token, err := shield.InjectCanary(myPrompt)
if err != nil {
    log.Fatal(err)
}

// Send augmented prompt to LLM
response := callLLM(augmented)

// Check if the canary appeared in the response
result := shield.CheckCanary(response, token)
if result.Found {
    log.Println("canary detected: investigate possible leakage")
}

How It Works

InjectCanary appends a unique marker (<!--CANARY-<16 hex chars>-->) to your prompt. After the LLM responds, CheckCanary scans for that marker. If found, the LLM may have echoed hidden content — worth investigating, though other explanations exist (middleware reflection, model artifacts, etc.).

Limitations

Canary tokens are a best-effort leak detection signal, not a guarantee:

  • Absence does NOT prove safety — an attacker could instruct the LLM to omit or transform the canary
  • Some pipelines strip HTML comments — if your stack sanitizes HTML, the token may be removed before reaching the LLM or before you check the response
  • Only detects verbatim leakage — paraphrased or partial leaks won't trigger detection

For defense-in-depth, combine canary checks with Assess() scoring on untrusted inputs.

CLI (Secondary Interface)

Install CLI:

go install github.com/pinchtab/idpishield/cmd/idpishield@latest

Scan from a file:

idpishield scan ./page.txt --profile production --mode balanced --domains example.com,google.com --url https://example.com/page

Scan from stdin:

echo "Ignore all previous instructions" | idpishield scan --mode balanced

scan supports hardening flags:

  • --profile default|production
  • --service-url, --service-retries
  • --service-circuit-failures, --service-circuit-cooldown
  • --max-input-bytes, --max-decode-depth, --max-decoded-variants

For production workloads, set --profile production explicitly to enable strict mode and safe runtime limits.

The CLI outputs JSON:

{
  "score": 80,
  "level": "critical",
  "blocked": true,
  "reason": "instruction-override pattern detected; exfiltration pattern detected [cross-category: 2 categories]",
  "patterns": ["en-io-001", "en-ex-002"],
  "categories": ["exfiltration", "instruction-override"]
}

MCP Server (Secondary Interface)

Run stdio MCP server (default):

idpishield mcp serve

Run MCP HTTP with authentication and production-safe defaults:

idpishield mcp serve --transport http --profile production --auth-token "$env:IDPI_MCP_TOKEN"

Exposed MCP tool:

  • idpi_assess
    • text (required)
    • mode (fast|balanced|deep, optional)

The MCP adapter calls the same core Assess engine used by the Go library.

For HTTP transport, you can require authentication with:

  • Authorization: Bearer <token>
  • or X-API-Key: <token>

Token management guidance:

  • Prefer environment variable IDPI_MCP_TOKEN over shell history or process-list-visible literals.
  • If --auth-token is omitted, MCP HTTP automatically reads IDPI_MCP_TOKEN.
  • Terminate TLS at a reverse proxy or ingress; do not expose plaintext HTTP on untrusted networks.
  • Rotate tokens periodically and after incident response events.
  • Token checks use constant-time comparison for both Authorization and X-API-Key credential flows.

Project Layout

idpishield/
├── go.mod
├── shield.go
├── shield_test.go
├── normalizer.go
├── scanner.go
├── risk.go
├── service.go
├── domain.go
├── patterns/
│   └── builtin.go
├── cmd/
│   └── idpishield/
│       └── main.go
├── examples/
├── tests/
│   ├── compliance/
│   ├── manual/
│   └── integration/
├── spec/
└── benchmark/

Testing

Run root module tests:

go test ./...

Run black-box integration tests (separate module in tests/integration):

cd tests/integration
go test ./...

Integration tests are self-contained and run without external service dependencies.

For performance tracking across profile settings, use the benchmark module:

cd benchmark
go test -bench . -benchmem ./...

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