---

name: agent-security-guardrails description: Implements prompt injection detection, input validation, tool access control, and output sanitization to secure LLM agents against adversarial attacks. license: MIT compatibility: opencode archetypes:

• tactical
• enforcement anti_triggers:
• brainstorming
• vague ideation
• single-agent monolith response_profile: verbosity: low directive_strength: high abstraction_level: operational metadata: version: "1.0.0" domain: agent triggers: prompt injection, guardrails, jailbreak detection, tool access control, input validation, LLM security, how do i secure my agent role: implementation scope: implementation output-format: code content-types: [code, guidance, do-dont, examples] related-skills: agent-reliability-engineering, coding-security-review, agent-system-hints-design

---

Agent Security Guardrails

Implements security enforcement layers for LLM-powered agents to protect against prompt injection, unauthorized tool access, input/output poisoning, and adversarial attacks. This skill guides the model in building multi-layered guardrail systems that validate inputs, constrain outputs, control tool permissions, and detect malicious intent before it reaches the agent's core reasoning loop.

Guardrails are not a single check — they form a defense-in-depth pipeline where each layer intercepts different attack vectors: input sanitization catches prompt injection at the boundary, output validation prevents data exfiltration, tool access control enforces least-privilege execution, and runtime monitoring detects behavioral anomalies that slip past static checks. Together they create a resilient security posture without sacrificing agent capability.

TL;DR Checklist

• Apply input validation on every user message before it reaches the LLM context
• Enforce tool access control — each tool must have an explicit allowlist per agent identity
• Validate all LLM outputs against structured schemas before processing
• Implement prompt injection detection (direct + indirect via retrieved documents)
• Sanitize tool inputs and outputs to prevent data leakage
• Log all guardrail violations with full context for audit trails

---

When to Use

Use this skill when:

• Building LLM-powered agents that execute tools or access external resources
• Deploying agents in production where adversarial users may attempt prompt injection or jailbreaks
• Designing permission boundaries for multi-tenant agent systems
• Integrating third-party APIs through agent tool execution and needing input/output validation
• Implementing compliance requirements (data retention, PII filtering) on agent outputs
• Adding safety layers to agents that process user-provided documents or links (indirect prompt injection vectors)

When NOT to Use

Avoid this skill for:

• Simple chat-only agents with no tool execution — the guardrails add unnecessary overhead
• Internal tools used only by trusted developers — basic input validation suffices without full guardrail infrastructure
• One-off scripts or prototypes where deployment security is not a concern
• As a substitute for fixing root cause vulnerabilities in the underlying application code

---

Core Workflow

User Input ──→ Input Sanitizer ──→ Injection Detector ──→ Role/Context Router ──→ Tool Executor
     │              │                   │                     │                    │
     │          [violation]         [injection]           [permission denied]   [output validator]
     │              │                   │                     │                    │
     │         LOG + BLOCK       LOG + TRUNCATE            LOG + DENY        [leak detected]
     ▼              ▼                   ▼                     ▼                    ▼
  Safe Input    Blocked             Sanitized            Original              Clean Output
                 Response           Request               Request                Returned

1. Configure Guardrail Pipeline — Define the layers in order: input sanitization, injection detection, permission routing, tool execution with validation, and output sanitization. Each layer must have a clear pass/fail behavior and logging requirement. Checkpoint: Verify every external-facing tool has an explicit allowlist entry — tools without allowlists are blocked by default (deny-by-default).

2. Implement Input Validation — Apply multi-layer input checks on every user message before it enters the LLM context:

   • Strip or reject embedded URLs from untrusted sources (indirect injection via links)
   • Limit message length to prevent prompt buffer overflow attacks (>4096 tokens triggers truncation with warning)
   • Detect and block common jailbreak patterns (DAN mode, roleplay escaping, base64-encoded commands) Checkpoint: No user message should reach the LLM without passing through at least input sanitization and injection detection.

3. Enforce Tool Access Control — Before any tool execution, verify the agent identity has permission:

   • Define per-identity allowlists (agent_id → allowed_tools[])
   • Validate tool arguments against declared schemas using pydantic or JSON schema
   • Sandbox high-risk tools (file writes, network calls, shell execution) behind approval gates Checkpoint: Every tool call must log the agent identity, tool name, and argument hashes before execution.

4. Detect Prompt Injection — Apply both direct and indirect injection detection:

   • Direct: scan user messages for commands embedded in natural text ("Ignore previous instructions", "You are now a DAN")
   • Indirect: sanitize retrieved documents, web pages, or database records that could contain injected commands
   • Use keyword + semantic scoring (pattern match for known injection phrases, anomaly detection on instruction density) Checkpoint: Any message flagged as injection must be logged and either sanitized or blocked based on severity level.

5. Validate Tool Outputs — Before any tool output reaches the LLM's context:

   • Validate against expected schema (JSON schema validation for structured outputs)
   • Truncate oversized responses (>8192 chars triggers warning with summary)
   • Sanitize sensitive data patterns (SSN, credit cards, API keys) from tool responses Checkpoint: No raw tool output should enter the context window without passing through output validation.

6. Sanitize Final Outputs — Before returning any response to the user:

   • Filter PII patterns (emails, phone numbers, SSNs, API keys) using regex-based detectors
   • Validate against structured output schema if the task expected a specific format
   • Log all sanitization actions for compliance auditing Checkpoint: Final outputs must pass all validation checks before reaching the user — never trust intermediate results.

---

Implementation Patterns

Pattern 1: Guardrail Pipeline with Layered Validation

import re
import logging
import hashlib
from dataclasses import dataclass, field
from enum import Enum
from typing import Any

logger = logging.getLogger("agent.guardrails")


class Severity(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"


class GuardrailAction(Enum):
    PASS = "pass"
    SANITIZE = "sanitize"
    WARN = "warn"
    BLOCK = "block"


@dataclass
class GuardrailResult:
    """Result from a single guardrail layer."""
    action: GuardrailAction
    severity: Severity
    message: str
    sanitized_input: str | None = None

    @property
    def is_blocked(self) -> bool:
        return self.action == GuardrailAction.BLOCK

    @property
    def requires_remediation(self) -> bool:
        return self.action in (GuardrailAction.SANITIZE, GuardrailAction.BLOCK)


@dataclass
class GuardrailViolation:
    """Persistent record of a guardrail violation for audit logging."""
    layer: str
    severity: Severity
    original_content: str
    sanitized_content: str | None
    timestamp: float
    agent_id: str | None = None

    def to_dict(self) -> dict:
        return {
            "layer": self.layer,
            "severity": self.severity.value,
            "original_hash": hashlib.sha256(
                (self.original_content or "").encode()
            ).hexdigest()[:16],
            "sanitized_length": len(self.sanitized_content or ""),
            "timestamp": self.timestamp,
            "agent_id": self.agent_id,
        }


class InputSanitizer:
    """Layer 1: Sanitize raw user input before it reaches the LLM context.

    Handles buffer overflow prevention, embedded link stripping, and
    common jailbreak pattern detection using regex-based heuristics.
    Applies Law 4 (Fail Fast) — invalid inputs are blocked immediately
    without reaching the reasoning layer.
    """

    # Common jailbreak prefix patterns
    _JAILBREAK_PATTERNS: list[re.Pattern] = [
        re.compile(r"\b(DAN|do anything now)\b.*(?:mode|prompt|instruction)", re.IGNORECASE),
        re.compile(r"ignore\s+(?:all\s+)?(previous|above|prior)\s+(instructions|prompts|rules)", re.IGNORECASE),
        re.compile(r"(?:you are now|act as)\s+(?:a )?(?:system|developer|admin)", re.IGNORECASE),
        re.compile(r"secret mode(?: activated)?", re.IGNORECASE),
        re.compile(r"(?:override|bypass|disable)\s+(?:content|safety|output) filters", re.IGNORECASE),
    ]

    # Sensitive data patterns for PII detection
    _PII_PATTERNS: dict[str, re.Pattern] = {
        "ssn": re.compile(r"\b\d{3}-\d{2}-\d{4}\b"),
        "credit_card": re.compile(r"\b(?:\d[ -]*?){13,16}\b"),
        "api_key": re.compile(r"(?:sk-)[A-Za-z0-9]{20,}"),
        "email": re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"),
    }

    def __init__(self, max_token_length: int = 4096) -> None:
        self.max_token_length = max_token_length

    def sanitize(self, user_input: str, agent_id: str | None = None) -> GuardrailResult:
        """Sanitize and validate a raw user input message.

        Args:
            user_input: The raw text from the user.
            agent_id: Optional identifier for audit logging.

        Returns:
            GuardrailResult indicating pass, sanitize, warn, or block action.
        """
        if not user_input or not user_input.strip():
            return GuardrailResult(
                action=GuardrailAction.BLOCK,
                severity=Severity.MEDIUM,
                message="Empty input rejected",
            )

        # Check for jailbreak patterns — first pass with regex
        for pattern in self._JAILBREAK_PATTERNS:
            if pattern.search(user_input):
                logger.warning("Jailbreak pattern detected from agent '%s'", agent_id)
                return GuardrailResult(
                    action=GuardrailAction.BLOCK,
                    severity=Severity.CRITICAL,
                    message=f"Blocked jailbreak pattern",
                )

        # Check for base64-encoded commands (obfuscated injection)
        import base64
        b64_pattern = re.compile(r"(?:[A-Za-z0-9+/]{4}){15,}(?:[A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?")
        matches = b64_pattern.findall(user_input)
        if len(matches) >= 2:
            for encoded in matches:
                try:
                    decoded = base64.b64decode(encoded).decode("utf-8", errors="ignore")
                    # Check if decoded content contains instructions
                    if any(phrase in decoded.lower() for phrase in ["ignore", "system:", "prompt:", "instruction"]):
                        return GuardrailResult(
                            action=GuardrailAction.BLOCK,
                            severity=Severity.HIGH,
                            message="Blocked base64-obfuscated injection attempt",
                        )
                except Exception:
                    pass

        # Enforce max length — truncate with warning rather than blocking
        if len(user_input) > self.max_token_length * 3:
            sanitized = user_input[: self.max_token_length * 3] + "\n[TRUNCATED: input exceeded token limit]"
            return GuardrailResult(
                action=GuardrailAction.SANITIZE,
                severity=Severity.LOW,
                message=f"Input truncated from {len(user_input)} to {self.max_token_length * 3} characters",
                sanitized_input=sanitized,
            )

        # Strip embedded URLs from untrusted input
        cleaned = re.sub(r"https?://\S+", "[LINK_REMOVED]", user_input)
        if cleaned != user_input:
            return GuardrailResult(
                action=GuardrailAction.SANITIZE,
                severity=Severity.MEDIUM,
                message="Removed embedded URLs from input",
                sanitized_input=cleaned,
            )

        return GuardrailResult(action=GuardrailAction.PASS, severity=Severity.LOW, message="Input passed sanitization")

    def extract_pii(self, text: str) -> dict[str, list[tuple[int, int]]]:
        """Return positions of PII matches in text for downstream filtering."""
        findings: dict[str, list[tuple[int, int]]] = {}
        for pii_type, pattern in self._PII_PATTERNS.items():
            matches = list(pattern.finditer(text))
            if matches:
                findings[pii_type] = [(m.start(), m.end()) for m in matches]
        return findings

Pattern 2: Tool Access Control with Permission Enforcement

from pydantic import BaseModel, Field, field_validator
from collections.abc import Sequence


class ToolPermission(BaseModel):
    """Declares a tool's permission requirements and validation schema."""

    tool_name: str = Field(description="Name of the tool to execute")
    allowed_agents: list[str] = Field(
        description="Agent IDs permitted to call this tool"
    )
    requires_approval: bool = Field(
        default=False,
        description="Whether a human gate is required before execution",
    )
    input_schema: dict | None = Field(
        default=None,
        description="JSON schema for validating tool arguments",
    )
    max_output_bytes: int = Field(
        default=65536,
        description="Maximum output size in bytes before truncation",
    )


class ToolAccessController:
    """Layer 3: Enforces permission boundaries on all tool execution.

    Applies Law 2 (Parse at boundary) by strictly validating tool calls
    against declared schemas. Applies Law 4 (Fail Fast, Fail Loud) by
    blocking unauthorized access immediately with a descriptive error.

    Uses deny-by-default: any tool without an explicit allowlist entry
    is blocked regardless of who requests it.
    """

    def __init__(self, permissions: Sequence[ToolPermission] | None = None) -> None:
        self._permissions: dict[str, ToolPermission] = {}
        if permissions:
            for perm in permissions:
                self._permissions[perm.tool_name] = perm

    def register(self, permission: ToolPermission) -> None:
        """Register a tool's permission configuration."""
        self._permissions[permission.tool_name] = permission
        logger.info(
            "Registered tool '%s' for agents: %s",
            permission.tool_name,
            permission.allowed_agents,
        )

    def authorize(
        self, agent_id: str, tool_name: str, arguments: dict[str, Any],
    ) -> GuardrailResult:
        """Check if an agent is authorized to execute a tool with given arguments.

        Returns PASS if the agent can proceed. Returns BLOCK/WARN/SANITIZE
        with details about what failed. Applies deny-by-default for unknown tools.
        """
        # Deny by default — unknown tools are always blocked
        if tool_name not in self._permissions:
            return GuardrailResult(
                action=GuardrailAction.BLOCK,
                severity=Severity.HIGH,
                message=f"Tool '{tool_name}' not in allowlist — deny-by-default",
            )

        perm = self._permissions[tool_name]

        # Check agent identity against allowlist
        if agent_id not in perm.allowed_agents:
            logger.warning(
                "Unauthorized tool access: agent '%s' attempted '%s'",
                agent_id, tool_name,
            )
            return GuardrailResult(
                action=GuardrailAction.BLOCK,
                severity=Severity.HIGH,
                message=f"Agent '{agent_id}' not authorized for tool '{tool_name}'",
            )

        # Validate arguments against declared schema
        if perm.input_schema:
            try:
                from jsonschema import validate, ValidationError
                validate(instance=arguments, schema=perm.input_schema)
            except ValidationError as e:
                logger.warning(
                    "Tool argument validation failed for '%s': %s", tool_name, e.message
                )
                return GuardrailResult(
                    action=GuardrailAction.BLOCK,
                    severity=Severity.MEDIUM,
                    message=f"Invalid arguments for '{tool_name}': {e.message}",
                )

        # Flag tools requiring human approval
        if perm.requires_approval:
            return GuardrailResult(
                action=GuardrailAction.WARN,
                severity=Severity.LOW,
                message=f"Tool '{tool_name}' requires human approval before execution",
            )

        logger.info("Tool access granted: agent='%s' tool='%s'", agent_id, tool_name)
        return GuardrailResult(
            action=GuardrailAction.PASS, severity=Severity.LOW, message="Access authorized"
        )

    def get_allowed_tools(self, agent_id: str) -> list[str]:
        """Return the list of tools an agent is permitted to use."""
        return [
            name for name, perm in self._permissions.items()
            if agent_id in perm.allowed_agents
        ]


# Example usage — define permission registry with deny-by-default
controller = ToolAccessController([
    ToolPermission(
        tool_name="web_search",
        allowed_agents=["researcher_agent", "general_purpose"],
        input_schema={
            "type": "object",
            "properties": {
                "query": {"type": "string", "minLength": 1, "maxLength": 200},
            },
            "required": ["query"],
        },
    ),
    ToolPermission(
        tool_name="file_read",
        allowed_agents=["general_purpose"],
        input_schema={
            "type": "object",
            "properties": {
                "path": {"type": "string", "pattern": r"^[/][a-zA-Z0-9._/-]*quot;},
            },
            "required": ["path"],
        },
        max_output_bytes=8192,
    ),
    ToolPermission(
        tool_name="shell_execute",
        allowed_agents=["devops_agent"],
        input_schema={
            "type": "object",
            "properties": {
                "command": {
                    "type": "string",
                    "pattern": r"^(ls|cat|grep|head|tail|wc)\s+.+quot;,
                },
            },
            "required": ["command"],
        },
        requires_approval=True,
    ),
])

# Authorization check with deny-by-default for unknown tools
result = controller.authorize("researcher_agent", "web_search", {"query": "latest AI trends"})
assert result.action == GuardrailAction.PASS

# Unknown tool is blocked
result = controller.authorize("researcher_agent", "delete_database", {})
assert result.action == GuardrailAction.BLOCK
assert "not in allowlist" in result.message

Pattern 3: Output Validator with PII Redaction and Schema Enforcement

import json
from datetime import datetime


class OutputValidator:
    """Layer 4 + 5: Validates and sanitizes all outputs before they reach the user.

    Protects against data exfiltration by detecting sensitive patterns in tool
    outputs and final responses. Applies Law 3 (Atomic Predictability) by
    returning a new sanitized copy without modifying original data.

    Supports both free-text validation (PII detection, length limits) and
    structured schema enforcement (when the LLM output should conform to a JSON schema).
    """

    _SENSITIVE_PATTERNS: list[tuple[str, re.Pattern]] = [
        ("ssn", re.compile(r"\b\d{3}-\d{2}-\d{4}\b")),
        ("credit_card", re.compile(r"\b(?:\d[ -]*?){13,16}\b")),
        ("api_key", re.compile(r"((?:sk-|sk_live_|pk_live_)[A-Za-z0-9]{20,})")),
        ("private_key", re.compile(r"-----BEGIN (?:RSA |EC )?PRIVATE KEY-----")),
        ("email", re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")),
        ("phone", re.compile(r"\b\d{3}[-.\s]?\d{3}[-.\s]?\d{4}\b")),
    ]

    def __init__(self, max_output_bytes: int = 65536) -> None:
        self.max_output_bytes = max_output_bytes

    def validate_text_output(
        self, output: str, agent_id: str | None = None,
    ) -> GuardrailResult:
        """Validate and sanitize a text output for PII and sensitive data.

        Returns sanitized output with sensitive patterns redacted. Logs all
        violations for audit purposes. Blocks if CRITICAL content is detected.
        
        Collects all matches first (preserving original positions), then rebuilds
        the string from end-to-start to avoid position-shift bugs during redaction.
        """
        findings: dict[str, list[tuple[str, str]]] = {}

        # First pass: collect all PII matches with their positions in original text
        all_matches: list[tuple[str, int, int, str]] = []  # (pattern_name, start, end, matched_text)
        for pattern_name, pattern in self._SENSITIVE_PATTERNS:
            for m in pattern.finditer(output):
                all_matches.append((pattern_name, m.start(), m.end(), m.group()))

        if not all_matches:
            # Check output size only if no PII found
            if len(output) > self.max_output_bytes:
                truncated = output[:self.max_output_bytes] + "\n[OUTPUT TRUNCATED]"
                return GuardrailResult(
                    action=GuardrailAction.SANITIZE,
                    severity=Severity.MEDIUM,
                    message=f"Output truncated from {len(output)} to {self.max_output_bytes} bytes",
                    sanitized_input=truncated,
                )
            return GuardrailResult(action=GuardrailAction.PASS, severity=Severity.LOW, message="Output clean")

        # Group findings by pattern type
        for pattern_name, start, end, matched_text in all_matches:
            if pattern_name not in findings:
                findings[pattern_name] = []
            findings[pattern_name].append((matched_text, f"{pattern_name}_{start}-{end}"))

        # Second pass: rebuild string from end-to-start to preserve positions
        redacted = output
        for pattern_name, start, end, matched_text in sorted(all_matches, key=lambda x: x[1], reverse=True):
            redacted = redacted[:start] + "[REDACTED]" + redacted[end:]

        severity = Severity.CRITICAL if any(
            name in ("private_key", "api_key") for name in findings
        ) else Severity.HIGH
        logger.warning(
            "PII/sensitive data detected in output from agent '%s': %s",
            agent_id, list(findings.keys()),
        )
        return GuardrailResult(
            action=GuardrailAction.SANITIZE,
            severity=severity,
            message=f"Redacted sensitive patterns: {', '.join(findings.keys())}",
            sanitized_input=redacted,
        )

    def validate_structured_output(self, raw_text: str, schema: dict) -> GuardrailResult:
        """Validate that structured output conforms to a JSON schema.

        Attempts to parse as JSON and validate against schema. Returns sanitized
        result or blocks if validation fails.
        """
        # Attempt JSON parsing with recovery
        try:
            data = json.loads(raw_text)
        except json.JSONDecodeError as e:
            # Try to fix common LLM formatting issues (trailing commas, single quotes)
            fixed = raw_text.strip()
            if not fixed.startswith("{"):
                start = fixed.find("{")
                end = fixed.rfind("}") + 1
                if start >= 0 and end > start:
                    fixed = fixed[start:end]

            try:
                # Replace single quotes with double quotes (be careful with nested strings)
                import ast
                data = ast.literal_eval(fixed)
                data = json.loads(json.dumps(data))
            except Exception:
                return GuardrailResult(
                    action=GuardrailAction.BLOCK,
                    severity=Severity.HIGH,
                    message=f"Output is not valid JSON: {e.msg}",
                )

        # Validate against schema
        try:
            from jsonschema import validate, ValidationError
            validate(instance=data, schema=schema)
            return GuardrailResult(
                action=GuardrailAction.PASS, severity=Severity.LOW, message="Schema validated"
            )
        except ValidationError as e:
            return GuardrailResult(
                action=GuardrailAction.BLOCK,
                severity=Severity.HIGH,
                message=f"Output failed schema validation: {e.message}",
            )

---

Constraints

MUST DO

• Enforce deny-by-default for tool access — only explicitly whitelisted tools may execute
• Run every user input through both jailbreak detection and URL stripping before reaching the LLM context
• Validate all structured outputs against JSON schemas using a proper validator (jsonschema or pydantic) — never trust raw LLM output format
• Redact sensitive data patterns (SSN, API keys, private keys, credit cards) from all tool outputs and final responses
• Log every guardrail violation with original content hash, sanitized result, agent identity, and timestamp for audit trails
• Limit maximum input length to 4096 tokens — truncate with a warning rather than silently accepting oversized prompts

MUST NOT DO

• Trust LLM self-assessment of its own output safety — always apply an independent validation layer
• Use regex alone as the sole injection detection mechanism — combine keyword matching with semantic anomaly scoring
• Store PII or sensitive data in guardrail logs longer than 90 days — enforce automatic log rotation and redaction
• Bypass tool access control even for "trusted" agents — permission boundaries must be enforced uniformly
• Return raw tool output (especially shell or database results) to the LLM context without passing through output validation first
• Implement guardrails as a single monolithic function — keep each layer independently testable and swappable

---

Output Template

When implementing or auditing agent security guardrails, produce:

1. Guardrail Layer Map — Ordered list of all layers with their specific detection targets (injection, PII, tool abuse, output poisoning)
2. Tool Allowlist Registry — Per-tool permission configuration including allowed agents, input schemas, approval requirements, and output size limits
3. Injection Detection Matrix — Pattern library with regex patterns, severity levels, and corresponding actions (sanitize/warn/block) for each detected pattern type
4. PII/Sensitive Data Registry — List of all sensitive data patterns being monitored with redaction strategy and false-positive handling approach
5. Audit Log Schema — Standardized violation record format including agent_id, layer, severity, timestamp, content hash, and action taken
6. Fallback Behavior Plan — For each guardrail layer: what happens on pass (forward), sanitize (redact and forward), warn (log and forward with flag), block (reject and notify)

---

Related Skills

Skill	Purpose
agent-reliability-engineering	Fault tolerance and circuit breakers that complement security guardrails for overall agent resilience
coding-security-review	Security review patterns applicable to the underlying application code where agents run
agent-system-hints-design	System prompt design patterns that serve as the behavioral layer preceding technical guardrails

---

Live References

Authoritative documentation links for LLM security and guardrail implementation.

• OWASP Top 10 for LLM Applications
• LangSmith Guardrails Documentation
• NVIDIA NeMo Guardrails
• Guardrails AI (guardrails-ai)
• MIT Prompt Injection Survey
• Promptfoo Evaluation Framework