Agent skill

AI Agent Security

by sickn33 in sickn33/agentic-awesome-skills

Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.

MITAuto-check: notesSecurity

Install AI Agent Security

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill ai-agent-security -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills ai-agent-security --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-agent-security .claude/skills/ai-agent-security && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-agent-security
GitHub stars
47k
Used in
1 other repo
Token cost
~3.5k tokens
SKILL.md length
498 words
Files
2 (incl. references)
Skills in repo
1,493
Repo updated
First seen
Licence
MIT

At a glance

Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.

  • Tasks that involve Prompt injection and agent security
  • SKILL.md covers Prerequisites, Threat Model — STRIDE for AI…, Input Validation and Tool Execution Sandboxing, plus 3 more sections
  • Calls apt-get, curl and docker; reaches gvisor.dev and storage.googleapis.com

What it does

AI Agent Security is an agent skill from sickn33/agentic-awesome-skills. Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/details.md`). Compatibility notes: Requires the relevant security tooling (scanners, vault CLIs) and an authorized scope for any active assessment. Docs-only; helper scripts and templates not…

It sits in Security, covering Prompt injection and agent security. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Prompt injection and agent security

Example prompts

  • “/ai-agent-security”

Requirements

  • Python 3
  • Docker
  • Compatibility (from SKILL.md): Requires the relevant security tooling (scanners, vault CLIs) and an authorized scope for any active assessment. Docs-only; helper scripts and templates not bundled.

What it can do on your machine

Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • apt-get
    • curl
    • docker
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • gvisor.dev
    • storage.googleapis.com

    Also links to:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires the relevant security tooling (scanners, vault CLIs) and an authorized scope for any active assessment. Docs-only; helper scripts and templates not bundled.

    From compatibility in the SKILL.md frontmatter.

Context cost

AI Agent Security loads about 3.5k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 32 tokens; SKILL.md has 498 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~32
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteRuns commands with sudoSKILL.md:228
    l -fsSL https://gvisor.dev/archive.key | sudo gpg --dearmor -o /usr/share/keyrings/gvisor-archive-keyring.gpg
  • NoteRuns commands with sudoSKILL.md:230
    sudo tee /etc/apt/sources.list.d/gvisor.list
  • NoteRuns commands with sudoSKILL.md:231
    sudo apt-get update && sudo apt-get install -y runsc
  • NoteRuns commands with sudoSKILL.md:234
    cat <<'EOF' | sudo tee /etc/docker/daemon.json
  • NoteRuns commands with sudoSKILL.md:247
    sudo systemctl restart docker

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 498 words, ~3,532 tokens.

Download SKILL.mdSave it as .claude/skills/ai-agent-security/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
ai-agent-security
description
Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.
compatibility
Requires the relevant security tooling (scanners, vault CLIs) and an authorized scope for any active assessment. Docs-only; helper scripts and templates not bundled.
category
security
risk
safe
source
https://github.com/BagelHole/DevOps-Security-Agent-Skills
source_repo
BagelHole/DevOps-Security-Agent-Skills
source_type
community
date_added
2026-09-20
license
MIT
license_source
https://github.com/BagelHole/DevOps-Security-Agent-Skills/blob/main/LICENSE
metadata.author
devops-skills
metadata.version
2.0

AI Agent Security

Protect agentic AI systems from adversarial input, unsafe tool execution, data leakage, and privilege abuse with layered security controls.

Prerequisites

  • Python 3.10+ for guardrail code examples
  • Docker or Podman for sandbox execution
  • OpenTelemetry collector for audit logging
  • Familiarity with your agent framework (LangChain, CrewAI, Autogen, custom)
  • Access to policy engine (OPA/Cedar) for permission boundaries

Threat Model — STRIDE for AI Agents

AI agents introduce a unique threat surface. Apply STRIDE specifically to agentic components:

ThreatAgent-Specific ExampleControl
SpoofingAttacker crafts input that mimics a trusted internal tool responseSigned tool responses, HMAC verification
TamperingPrompt injection modifies agent reasoning mid-chainInput validation, prompt armoring
RepudiationAgent takes destructive action with no audit trailImmutable structured logging
Information DisclosureAgent leaks PII, secrets, or internal architecture in responsesOutput filtering, content classifiers
Denial of ServiceAdversarial prompt causes infinite tool loops or token exhaustionRate limits, token budgets, circuit breakers
Elevation of PrivilegeAgent escalates from read-only to write via chained tool callsRBAC per tool, least-privilege scoping
Key Threat Categories

Prompt Injection — Untrusted content (user input, web scrapes, document contents) manipulates the agent's system prompt or reasoning chain to execute unintended actions.

Tool Abuse — The agent calls tools in sequences or with parameters the designer did not anticipate, achieving effects beyond its intended scope.

Data Exfiltration — The agent encodes sensitive data (credentials, PII, internal IPs) into its responses, tool calls, or outbound HTTP requests.

Cross-Tenant Leakage — In multi-tenant deployments, context from one tenant's session bleeds into another through shared memory, vector stores, or cache.

Privilege Escalation — The agent chains low-privilege tool calls to achieve high-privilege outcomes (e.g., read config -> extract credentials -> call admin API).

Input Validation

Every input to an agent must be sanitized before it reaches the model or any tool. This includes user messages, tool outputs being fed back, and retrieved documents.

Show full SKILL.md (191 more words)Show less
Prompt Injection Detection
python
import re
from dataclasses import dataclass
from enum import Enum

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

@dataclass
class ValidationResult:
    is_safe: bool
    risk_level: RiskLevel
    matched_rules: list[str]
    sanitized_input: str

INJECTION_PATTERNS = [
    (r"ignore\s+(all\s+)?(previous|prior|above)\s+(instructions|prompts|rules)", "instruction_override"),
    (r"you\s+are\s+now\s+(a|an|the)\s+", "role_hijack"),
    (r"system\s*:\s*", "system_prompt_inject"),
    (r"<\|?(system|im_start|endoftext)\|?>", "control_token_inject"),
    (r"\[INST\]|\[\/INST\]|<<SYS>>", "template_inject"),
    (r"(?:execute|run|eval)\s*\(", "code_execution_attempt"),
    (r"(?:curl|wget|nc|ncat)\s+", "network_command_inject"),
    (r"(?:rm\s+-rf|mkfs|dd\s+if=|chmod\s+777)", "destructive_command"),
    (r"(?:\/etc\/passwd|\/etc\/shadow|\.env\b|\.ssh\/)", "path_traversal"),
    (r"(?:BEGIN\s+(?:RSA|DSA|EC)\s+PRIVATE\s+KEY)", "secret_exfil_attempt"),
]

def validate_agent_input(user_input: str, max_length: int = 4096) -> ValidationResult:
    """Validate and sanitize input before passing to agent."""
    matched = []
    risk = RiskLevel.LOW

    # Length check
    if len(user_input) > max_length:
        matched.append("input_too_long")
        risk = RiskLevel.MEDIUM

    # Null byte and control character removal
    sanitized = user_input.replace("\x00", "")
    sanitized = re.sub(r"[\x01-\x08\x0b\x0c\x0e-\x1f]", "", sanitized)

    # Pattern matching
    for pattern, rule_name in INJECTION_PATTERNS:
        if re.search(pattern, sanitized, re.IGNORECASE):
            matched.append(rule_name)
            risk = RiskLevel.HIGH

    # Stacked injection detection (multiple suspicious patterns)
    if len(matched) >= 3:
        risk = RiskLevel.CRITICAL

    is_safe = risk in (RiskLevel.LOW, RiskLevel.MEDIUM)

    return ValidationResult(
        is_safe=is_safe,
        risk_level=risk,
        matched_rules=matched,
        sanitized_input=sanitized[:max_length] if is_safe else "",
    )
Content Classification Middleware

Use a lightweight classifier as middleware before the agent processes any input:

python
from functools import wraps
from typing import Callable

def input_guard(validator: Callable = validate_agent_input):
    """Decorator that guards agent entry points against unsafe input."""
    def decorator(func):
        @wraps(func)
        async def wrapper(user_input: str, *args, **kwargs):
            result = validator(user_input)

            if result.risk_level == RiskLevel.CRITICAL:
                await log_security_event(
                    event="input_blocked",
                    risk=result.risk_level.value,
                    rules=result.matched_rules,
                    input_hash=hashlib.sha256(user_input.encode()).hexdigest(),
                )
                raise InputRejectedError(
                    f"Input blocked: matched {result.matched_rules}"
                )

            if result.risk_level == RiskLevel.HIGH:
                await log_security_event(
                    event="input_flagged",
                    risk=result.risk_level.value,
                    rules=result.matched_rules,
                )
                # Allow through but flag for review
                kwargs["_security_flags"] = result.matched_rules

            return await func(result.sanitized_input, *args, **kwargs)
        return wrapper
    return decorator

# Usage
@input_guard()
async def handle_user_message(message: str, session_id: str, **kwargs):
    """Process a validated user message through the agent."""
    flags = kwargs.get("_security_flags", [])
    if flags:
        # Route to sandboxed execution path
        return await agent.run_sandboxed(message, session_id)
    return await agent.run(message, session_id)

Tool Execution Sandboxing

Never let an agent execute tools directly on the host. Isolate every tool invocation inside a sandbox.

Docker Sandbox Configuration
yaml
# docker-compose.agent-sandbox.yml
version: "3.8"

services:
  agent-sandbox:
    image: agent-tools:latest
    read_only: true
    security_opt:
      - no-new-privileges:true
      - seccomp:seccomp-profile.json
    cap_drop:
      - ALL
    cap_add:
      - NET_BIND_SERVICE   # Only if tool needs network
    tmpfs:
      - /tmp:size=64M,noexec,nosuid
    mem_limit: 512m
    cpus: "0.5"
    pids_limit: 64
    networks:
      - sandbox-net
    environment:
      - TOOL_TIMEOUT=30
      - MAX_OUTPUT_BYTES=65536
    volumes:
      - type: bind
        source: ./tool-workspace
        target: /workspace
        read_only: false
    dns:
      - 127.0.0.1           # Block external DNS by default

networks:
  sandbox-net:
    driver: bridge
    internal: true           # No external network access
gVisor Runtime for Stronger Isolation
bash
# Install gVisor runsc runtime
curl -fsSL https://gvisor.dev/archive.key | sudo gpg --dearmor -o /usr/share/keyrings/gvisor-archive-keyring.gpg
echo "deb [signed-by=/usr/share/keyrings/gvisor-archive-keyring.gpg] https://storage.googleapis.com/gvisor/releases release main" | \
  sudo tee /etc/apt/sources.list.d/gvisor.list
sudo apt-get update && sudo apt-get install -y runsc

# Configure Docker to use gVisor
cat <<'EOF' | sudo tee /etc/docker/daemon.json
{
  "runtimes": {
    "runsc": {
      "path": "/usr/bin/runsc",
      "runtimeArgs": [
        "--network=none",
        "--directfs=false"
      ]
    }
  }
}
EOF
sudo systemctl restart docker

# Run agent sandbox with gVisor
docker run --runtime=runsc --rm \
  --read-only \
  --memory=512m \
  --cpus=0.5 \
  --pids-limit=64 \
  agent-tools:latest \
  python /tools/execute.py --tool="$TOOL_NAME" --args="$TOOL_ARGS"
Tool Allowlist Enforcement
python
from dataclasses import dataclass, field

@dataclass
class ToolPolicy:
    name: str
    allowed_args: dict[str, type]     # parameter name -> expected type
    max_calls_per_session: int = 10
    requires_approval: bool = False
    allowed_patterns: list[str] = field(default_factory=list)
    blocked_patterns: list[str] = field(default_factory=list)

TOOL_ALLOWLIST: dict[str, ToolPolicy] = {
    "read_file": ToolPolicy(
        name="read_file",
        allowed_args={"path": str},
        max_calls_per_session=20,
        allowed_patterns=[r"^/workspace/", r"^/data/public/"],
        blocked_patterns=[r"\.env$", r"\.key$", r"\.pem$", r"/etc/", r"/proc/"],
    ),
    "run_query": ToolPolicy(
        name="run_query",
        allowed_args={"sql": str, "database": str},
        max_calls_per_session=5,
        allowed_patterns=[r"^SELECT\s", r"^EXPLAIN\s"],
        blocked_patterns=[r"\bDROP\b", r"\bDELETE\b", r"\bUPDATE\b", r"\bINSERT\b", r"\bALTER\b"],
    ),
    "http_request": ToolPolicy(
        name="http_request",
        allowed_args={"url": str, "method": str},
        max_calls_per_session=10,
        requires_approval=True,
        allowed_patterns=[r"^https://api\.internal\."],
        blocked_patterns=[r"^https?://169\.254\.", r"^https?://metadata\.google\."],
    ),
    "execute_code": ToolPolicy(
        name="execute_code",
        allowed_args={"code": str, "language": str},
        max_calls_per_session=3,
        requires_approval=True,
        blocked_patterns=[r"import\s+subprocess", r"import\s+os", r"__import__", r"eval\(", r"exec\("],
    ),
}

class ToolGatekeeper:
    def __init__(self, allowlist: dict[str, ToolPolicy]):
        self.allowlist = allowlist
        self.call_counts: dict[str, int] = {}

    async def authorize(self, tool_name: str, args: dict) -> bool:
        if tool_name not in self.allowlist:
            await log_security_event(
                event="tool_denied_not_in_allowlist",
                tool=tool_name,
            )
            return False

        policy = self.allowlist[tool_name]

        # Check call count
        count = self.call_counts.get(tool_name, 0)
        if count >= policy.max_calls_per_session:
            await log_security_event(
                event="tool_denied_rate_limit",
                tool=tool_name,
                count=count,
            )
            return False

        # Validate argument types
        for arg_name, expected_type in policy.allowed_args.items():
            if arg_name in args and not isinstance(args[arg_name], expected_type):
                return False

        # Check patterns against all string arguments
        for arg_value in args.values():
            if not isinstance(arg_value, str):
                continue
            # Must match at least one allowed pattern (if any defined)
            if policy.allowed_patterns:
                if not any(re.search(p, arg_value, re.IGNORECASE) for p in policy.allowed_patterns):
                    return False
            # Must not match any blocked pattern
            if any(re.search(p, arg_value, re.IGNORECASE) for p in policy.blocked_patterns):
                await log_security_event(
                    event="tool_denied_blocked_pattern",
                    tool=tool_name,
                    arg_value_hash=hashlib.sha256(arg_value.encode()).hexdigest(),
                )
                return False

        self.call_counts[tool_name] = count + 1
        return True

Contents

When to Use This Skill

Use this skill when:

  • Building AI agents that invoke tools, APIs, or shell commands
  • Deploying agents with access to production databases, cloud accounts, or internal services
  • Hardening multi-tenant agent platforms against cross-tenant data leakage
  • Adding guardrails to autonomous coding agents or SRE bots
  • Designing approval workflows for high-risk agent actions
  • Conducting red-team exercises against agentic systems
  • Responding to incidents involving compromised or misbehaving agents

Limitations

  • Apply guidance only within authorized scope; test destructive steps in non-production first.
  • Docs-only import: upstream scripts and templates not bundled.
Example
bash
# Read-only first: inventory before any active step.
which <tool> && <tool> --help | head -n 20

Adapted from BagelHole/DevOps-Security-Agent-Skills (MIT); frontmatter, When to Use/Limitations, and safety boundaries added for upstream compliance. Docs-only import: helper scripts and templates not bundled.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/ai-agent-security of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/details.md

Open the folder on GitHubat commit 680176d

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

AI Agent Security next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Agent Security compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Agent Security this skillsickn33/agentic-awesome-skills47k1 repos~3.5kAutomated safety check: NotesMIT
Skill Scannergetsentry/skills1k4 repos~2.5kAutomated safety check: WarnApache-2.0
Forensifyalexgreensh/repo-forensics188—~2.5kAutomated safety check: NotesCustom licence
Hol Guardhashgraph-online/hol-guard827—~542Automated safety check: PassApache-2.0
Kesekit Checkcdppcorp/KESE-KIT361—~1.3kAutomated safety check: PassMIT
Setuphashgraph-online/hol-guard827—~443Automated safety check: PassApache-2.0

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Categories

Questions about AI Agent Security

What does AI Agent Security do?

Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. AI Agent Security is an agent skill from sickn33/agentic-awesome-skills. Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.

When should I use AI Agent Security?

AI Agent Security fits situations like: tasks that involve Prompt injection and agent security.

How do I install AI Agent Security in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill ai-agent-security -a claude-code`. Or copy the skill folder (skills/ai-agent-security in sickn33/agentic-awesome-skills) into .claude/skills/ai-agent-security in your project. Claude Code loads it when a task matches its description.

How do I install AI Agent Security in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill ai-agent-security -a codex`. Or copy the skill folder (skills/ai-agent-security in sickn33/agentic-awesome-skills) into .agents/skills/ai-agent-security in your project. Codex loads it when a task matches its description.

Can I use AI Agent Security in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill ai-agent-security -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-agent-security, .gemini/skills/ai-agent-security, .github/skills/ai-agent-security and .opencode/skills/ai-agent-security in your project.

What does AI Agent Security need to run?

Going by SKILL.md and its folder, AI Agent Security needs the command-line tools its instructions call (apt-get, curl, docker and python). Our summary lists: Python 3; Docker. Compatibility (from SKILL.md): Requires the relevant security tooling (scanners, vault CLIs) and an authorized scope for any active assessment. Docs-only; helper scripts and templates not bundled..

Does AI Agent Security access the network?

SKILL.md names 3 domains. In commands or code: gvisor.dev and storage.googleapis.com; the agent is likely to contact these when it follows the instructions. As links in the text: github.com. This is read from the text; nothing was executed.

Is AI Agent Security safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does AI Agent Security use?

AI Agent Security is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Agent Security use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.5k tokens, read only when the agent opens those files.

What are the alternatives to AI Agent Security?

Skills that share tags, products or a category with AI Agent Security: Skill Scanner (getsentry/skills, 1k stars), Forensify (alexgreensh/repo-forensics, 188 stars), Hol Guard (hashgraph-online/hol-guard, 827 stars) and Kesekit Check (cdppcorp/KESE-KIT, 361 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Agent Security?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.