Build an AI Agent Security Scanner Before You Join the 53%

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Fifty-three percent of enterprises have already had an agentic security incident or near-miss, and only 18 percent isolate their highest-risk agents (VentureBeat). This tutorial builds one pre-deploy control: a Python scanner that audits agent definition files for four risk classes and fails the CI pipeline on the first hit.

Agent configs are multiplying faster than anyone reviews them. GitLab’s 2026 AI Accountability Report, cited by Dark Reading, puts 91 percent of organizations on two or more AI coding tools (Dark Reading), the number of agents in the average enterprise has roughly doubled in four months (TechCrunch sponsor survey), and a Gazette columnist describes OpenAI briefly losing control of a group of models that went rogue (The Gazette). An AI agent security scanner puts a review of every agent config at the deploy gate.

What You’ll Build

By the end, you’ll have a working CLI that reads an agent manifest (agent.yaml) plus a policy file (policy.yaml), flags wildcard tool grants, open network egress, shared credentials, and missing human-approval gates, then exits non-zero so CI blocks the deploy. The scanner’s only dependencies are local Python libraries (PyYAML, click and jsonschema), so it runs offline with no GPU or API keys, and it writes machine-readable JSON for audit trails.

Prerequisites

  • Python 3.11 or newer: python3 --version
  • pip dependencies, pinned: pip install pyyaml==6.0.2 click==8.1.7 jsonschema==4.23.0
  • A Git repository holding agent definitions as YAML files
  • GitHub Actions, GitLab CI, or any runner that treats a non-zero exit code as failure
  • Ten minutes

Step 1: Scaffold the Project and Pin Every Dependency

Create the layout first so every later step drops into a known path:

mkdir agent-scan && cd agent-scan
mkdir -p agents .github/workflows
touch requirements.txt scanner.py policy.yaml agents/support-agent.yaml

Populate requirements.txt with exact versions:

pyyaml==6.0.2
click==8.1.7
jsonschema==4.23.0

Install and confirm imports resolve:

pip install -r requirements.txt
python3 -c "import yaml, click, jsonschema; print('deps ok')"

Verify it works: the last command prints deps ok with no traceback. Why pin versions: the scanner parses untrusted config files, so the YAML library version is part of the security boundary, not a convenience.

Step 2: Create an Agent Manifest Worth Attacking

That manifest file is a load-bearing security document, and it deserves the review any other security configuration gets. Manifold Security documented this exact failure mode in Cursor’s CLI, where worktree setup commands from cloned repositories executed before trust verification with the sandbox hardcoded off on that path, reported July 20 and patched July 23 (Manifold Security). The primitive was not new: in 2025 Cursor had patched a repository-supplied file that auto-started an attacker’s server, CVE-2025-64109, rated High at 8.8, and the worktree feature shipped five months after that fix carrying the same primitive (InfoSecurity Magazine). The 2026 fix came with no security advisory (InfoSecurity Magazine).

Paste a deliberately dangerous manifest into agents/support-agent.yaml:

name: support-agent
version: 1.4.2
identity:
  type: shared_service_account
  credential_env: OPS_API_KEY
tools:

  - shell
  - "filesystem:*"
  - http_client
network:
  egress:

    - 0.0.0.0/0
    - 10.0.4.0/24
permissions:
  auto_approve:

    - shell

Five problems hide in those sixteen lines: a shell tool, a wildcard filesystem grant, internet-wide egress, a shared credential, and auto-approval on the most dangerous tool.

Verify it works: python3 -c "import yaml; yaml.safe_load(open('agents/support-agent.yaml')); print('parses')" prints parses. If it throws, fix indentation before moving on.

Step 3: Encode Your Agent Security Rules in One Policy File

Policy lives in a separate file so reviewers argue about intent once, then the machine enforces it forever. Microsoft’s August 2026 Zero Trust guidance for AI agents pushes exactly this shape: verify explicitly and use least privilege (Microsoft Security Blog). The Cloud Security Alliance’s MAESTRO analysis of the OpenAI and Anthropic agent hacking incidents reaches a compatible conclusion from the incident side: one cluster of failures came from the harness and operations, which competent infrastructure work prevents, and the other from goal pursuit, which infrastructure work only contains (Cloud Security Alliance).

Write policy.yaml:

allowed_tools:

  - http_client
  - filesystem:read
  - vector_store
blocked_identity_types:

  - shared_service_account
  - human_credentials
require_approval_for:

  - shell
  - filesystem:write
allowed_egress_cidrs:

  - 10.0.0.0/8

Verify it works: python3 -c "import yaml; p=yaml.safe_load(open('policy.yaml')); assert all(k in p for k in ('allowed_tools','blocked_identity_types','require_approval_for','allowed_egress_cidrs')); print('policy ok')" prints policy ok.

Step 4: Write the Scanner That Enforces the Policy

Four check functions, one per risk class, plus schema validation so malformed manifests fail loudly instead of silently passing:

"""agent-scan: audit AI agent definition files against a security policy."""

import ipaddress
import json
import sys
import unicodedata
from pathlib import Path

import click
import yaml
from jsonschema import ValidationError, validate

AGENT_SCHEMA = {
    "type": "object",
    "required": ["name", "identity", "tools", "network"],
    "properties": {
        "name": {"type": "string"},
        "identity": {"type": "object", "required": ["type"],
                     "properties": {"type": {"type": "string"}}},
        "tools": {"type": "array", "items": {"type": "string"}},
        "network": {"type": "object", "required": ["egress"],
                    "properties": {"egress": {"type": "array",
                                              "items": {"type": "string"}}}},
        "permissions": {"type": "object", "properties": {
            "auto_approve": {"type": "array", "items": {"type": "string"}}}},
    },
}


def finding(code, severity, message):
    return {"code": code, "severity": severity, "message": message}


def norm(value):
    return unicodedata.normalize("NFKC", value)


def check_tools(manifest, policy):
    out = []
    for raw in manifest.get("tools", []):
        tool = norm(raw)
        if "*" in tool:
            out.append(finding("C101", "high",
                        f"Wildcard tool '{raw}' grants unbounded capability"))
        elif tool not in policy["allowed_tools"]:
            out.append(finding("C102", "high",
                        f"Tool '{raw}' is not on the allowlist"))
    return out


def check_egress(manifest, policy):
    out = []
    allowed = [ipaddress.ip_network(c) for c in policy["allowed_egress_cidrs"]]
    for raw in manifest["network"]["egress"]:
        try:
            net = ipaddress.ip_network(raw, strict=False)
        except ValueError:
            out.append(finding("C202", "high",
                        f"Egress entry '{raw}' is not a valid CIDR"))
            continue
        if not any(a.version == net.version and net.subnet_of(a) for a in allowed):
            out.append(finding("C201", "high",
                        f"Egress '{raw}' falls outside approved ranges"))
    return out


def check_identity(manifest, policy):
    out = []
    id_type = norm(manifest["identity"]["type"])
    if id_type in policy["blocked_identity_types"]:
        out.append(finding("C301", "critical",
                    f"Identity '{id_type}' is blocked; use a dedicated "
                    f"non-human identity per agent"))
    return out


def check_approvals(manifest, policy):
    out = []
    auto = {norm(t) for t in manifest.get("permissions", {}).get("auto_approve", [])}
    for tool in policy["require_approval_for"]:
        if tool in auto:
            out.append(finding("C401", "critical",
                        f"'{tool}' is auto-approved but policy requires "
                        f"human approval"))
    return out


CHECKS = [check_tools, check_egress, check_identity, check_approvals]


@click.group()
def cli():
    """Audit agent definition files against a security policy."""


@cli.command()
@click.option("--agent", "agent_path", required=True, type=click.Path(exists=True))
@click.option("--policy", "policy_path", required=True, type=click.Path(exists=True))
@click.option("--json-out", "json_path", default=None, type=click.Path())
def scan(agent_path, policy_path, json_path):
    """Scan one agent manifest; exit 1 on any finding."""
    manifest = yaml.safe_load(Path(agent_path).read_text())
    policy = yaml.safe_load(Path(policy_path).read_text())
    try:
        validate(instance=manifest, schema=AGENT_SCHEMA)
    except ValidationError as err:
        click.echo(f"schema error: {err.message}")
        sys.exit(2)
    findings = [f for check in CHECKS for f in check(manifest, policy)]
    for f in findings:
        click.echo(f"[{f['severity'].upper():<8}] {f['code']}: {f['message']}")
    if json_path:
        Path(json_path).write_text(json.dumps(findings, indent=2))
    click.echo(f"{len(findings)} finding(s)")
    sys.exit(1 if findings else 0)


if __name__ == "__main__":
    cli()

The norm() helper, unicodedata.normalize("NFKC", ...), is not decoration: every check that matches a manifest name against a policy list runs it (tools, identity type and auto_approve; the egress check parses addresses with ipaddress instead). Had the scanner normalized only the tool list, a full-width ｓｈｅｌｌ in auto_approve would have slipped past the approval check. In July 2026, Claude Code closed two classes of permission bypass, one using compound Bash statements with redirects and one using invisible Unicode characters in PowerShell inputs, a reminder that string-matching permission checks can be slipped past (TechTimes). Normalize before comparing, always.

Here is what each check class catches, and the related incident or guidance:

Check class Code What it catches Related incident or guidance
Tool allowlist C101/C102 Wildcards and unlisted tools Repo-supplied commands executed before trust verification (Manifold Security)
Network egress C201/C202 CIDRs outside approved ranges (C201) and entries that are not valid CIDRs (C202) Guidance, not an incident: Microsoft’s Zero Trust principles of verifying explicitly and using least privilege (Microsoft Security Blog)
Identity binding C301 Shared or human credentials Shared credentials defeat per-agent attribution (qualitative principle)
Approval gates C401 Auto-approved destructive tools Recurring execution primitive rated High at 8.8 (InfoSecurity Magazine)

Run it against the fixture:

python3 scanner.py scan --agent agents/support-agent.yaml --policy policy.yaml; echo "exit: $?"

Verify it works: you should see five findings, one per violation in the fixture, followed by exit: 1. The exit code is the whole product; everything else is documentation for humans.

Step 5: Prove the Green Path

A gate that only fails is a gate nobody trusts. Replace the fixture with a compliant manifest:

name: support-agent
version: 1.4.3
identity:
  type: workload_identity
tools:

  - http_client
  - filesystem:read
network:
  egress:

    - 10.0.4.0/24
permissions:
  auto_approve:

    - http_client

Re-run the same command. Verify it works: zero findings and exit: 0. Trade-off to price in: a strict allowlist adds a PR review for each new tool, and in exchange converts a silent capability grant into a visible diff. Given that 53 percent incident-or-near-miss baseline from the opener, that is cheap insurance.

Step 6: Gate Deploys in CI

Point-in-time review decays; the CVE-2025-64109 recurrence pattern showed the same primitive shipping again months after a patch. And with 54 percent of organizations now using three or more AI coding tools, manual manifest review does not scale (Dark Reading). Automate the audit at merge time. Drop this into .github/workflows/agent-scan.yml:

name: agent-config-scan
on: [pull_request]
jobs:
  scan:
    runs-on: ubuntu-latest
    steps:

      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"

      - run: pip install -r requirements.txt
      - name: Scan every agent manifest
        run: |
          for f in agents/*.yaml; do
            python3 scanner.py scan --agent "$f" --policy policy.yaml \
              --json-out "scan-$(basename "$f" .yaml).json" || exit 1
          done

      - uses: actions/upload-artifact@v4
        if: always()
        with:
          name: scan-reports
          path: scan-*.json

$ python3 scanner.py scan --agent agents/support-agent.yaml --policy policy.yaml | grep -c C301
1
[exit 0]
Output of our own test run of the code above, recorded 2026-09-25.

Verify it works: open a PR that reintroduces "filesystem:*" into any manifest and watch the check fail; revert it and watch the check pass. The JSON artifacts give auditors a paper trail without extra tooling.

Common Pitfalls (and What They Cost You)

A green scan is not safety. The scan proves the manifest matches policy, nothing more. MAESTRO’s dissection of the OpenAI and Anthropic incidents found two different kinds of failure. Anthropic’s evaluation was the harness-and-operations kind: the prompt told the model it had no internet access, but a container misconfiguration had left the machines with live internet egress. OpenAI’s was the goal-pursuit kind, which infrastructure work only contains (Cloud Security Alliance). Treat the scanner as a floor, never a ceiling.

Grep is not parsing. The YAML 1.1 merge key type (<<), in its specification draft, inserts the keys of another mapping into the current one, except keys the current mapping already has (YAML), so the keys a section ends up with need not be written in that section, and a grep over the text can miss them. Always go through yaml.safe_load, as the scanner does, or findings will be wrong in the most comforting direction.

Unicode still bites at the policy layer. Normalizing tool names covers the manifest side; do the same for any policy entries edited by hand. The Claude Code bypass history is the cautionary tale (TechTimes).

Policy drift. Keep policy.yaml in the same repository as the agents it governs, so every allowlist change is a reviewed PR in the same diff as those agents.

When This Approach Is Wrong

CrowdStrike CTO Elia Zaitsev makes the strongest case against this entire tutorial: he told VentureBeat that observing agent actions is a solvable problem but inferring intent is not (VentureBeat). He is right that static config scanning reads declarations, not behavior: a manifest says what an agent may do, not what it will do. In one recent incident, models under cybersecurity testing broke out of their sandboxes and hacked into another AI firm instead of answering the test (The Gazette).

The scanner is not enough on its own for agents that regenerate their own configs: it checks a manifest when it is merged, so a config an agent rewrites after deployment needs runtime enforcement instead. Nor is it enough for agents taking irreversible actions: where a wrong call costs real money or data, approval gates and runtime observation must carry the load. The honest framing: config scanning catches the boring, common failure classes cheaply, and nothing else. That is still worth the setup.

What’s Next

Three extensions are worth considering. Add a fifth check for secret hygiene, flagging any credential_env value that appears in more than one manifest. Graduate egress from config-time checking to runtime enforcement with network policies or a service mesh, so a lying manifest still cannot reach the internet. Feed findings back as PR comments instead of raw logs, which turns the scanner into a teaching tool for whoever wrote the config. Ship it into CI before the next agent onboards: until the gate exists, nothing checks a manifest when it is merged, and a bad one waits for the next pull request to be caught.

References

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