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Security audit

Cloud Tag Enforcer

Security checks for vulnerabilities and agentic risk

Overview

The skill has a legitimate cloud-tagging purpose, but its examples include broad live-cloud remediation scripts and unsafe command patterns that users should review carefully before installing.

Install only if you are comfortable reviewing cloud-governance automation before use. Run scan/report commands with least-privileged read-only credentials where possible, do not run generated remediation scripts directly against production, replace placeholder tag values with real approved values, narrow the account/project/resource scope, and sandbox repository scans from untrusted Terraform code.

Vulnerability Patterns
  • Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
  • Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
Findings (2)

T09 · Insecure Skill Coding Practices

Error
Location
SKILL.md:434
Finding
Arbitrary Code Execution Through Unsafe Terraform Filename Interpolation## Vulnerability Details **File Location**: `SKILL.md`, lines 434-463 **Vulnerability Type**: Command and Python code injection through an untrusted filename **Risk Level**: High ### Vulnerable Code ```bash # Find Terraform resource blocks missing tags rg -l 'resource "aws_' . -g '*.tf' | while read tf_file; do python3 -c " import re with open('$tf_file') as f: content = f.read() blocks = re.finditer(r'resource\s+\"(aws_\w+)\"\s+\"(\w+)\"\s*\{', content) for match in blocks: rtype, rname = match.group(1), match.group(2) # Find the block end by counting braces start = match.end() depth = 1 pos = start while depth > 0 and pos < len(content): if content[pos] == '{': depth += 1 elif content[pos] == '}': depth -= 1 pos += 1 block = content[start:pos] if 'tags' not in block: print(f'MISSING tags in $tf_file: {rtype}.{rname}') " done ``` ### Technical Analysis The filename returned by `rg` is assigned to the shell variable `tf_file` and then interpolated directly into the source code passed to `python3 -c`. The interpolation occurs inside a shell double-quoted argument and inside a Python single-quoted string: ```python with open('$tf_file') as f: ``` A repository controls its filenames. A crafted Terraform filename containing quote characters, line breaks, shell substitutions, or Python syntax can therefore terminate or modify the intended Python string. This allows the filename to change the Python program executed during the scan. The loop also uses `while read tf_file` without null-delimited paths or `IFS= read -r`, making handling of backslashes, whitespace, and newline characters unsafe. ### Attack Path 1. An attacker prepares a repository containing an `.tf` file whose contents match `resource "aws_`, ensuring that `rg` returns its filename. 2. The attacker gives the file a specially crafted name containing ...[truncated 1171 chars]
Remediation
## Remediation Suggestions Never interpolate a repository-controlled path into shell or Python source code. Pass the filename as a positional argument and use null-delimited path handling: ```bash rg -l -0 'resource "aws_' . -g '*.tf' | while IFS= read -r -d '' tf_file; do python3 - "$tf_file" <<'PY' import re import sys file_path = sys.argv[1] with open(file_path, encoding="utf-8") as f: content = f.read() blocks = re.finditer( r'resource\s+"(aws_\w+)"\s+"(\w+)"\s*\{', content, ) for match in blocks: rtype, rname = match.group(1), match.group(2) start = match.end() depth = 1 pos = start while depth > 0 and pos < len(content): if content[pos] == "{": depth += 1 elif content[pos] == "}": depth -= 1 pos += 1 block = content[start:pos] if "tags" not in block: print(f"MISSING tags in {file_path}: {rtype}.{rname}") PY done ``` Additional hardening should include running repository scans in a sandbox without production credentials, testing the scanner against filenames containing quotes and newlines, and avoiding source-code generation from untrusted values.

T09 · Insecure Skill Coding Practices

Warning
Location
SKILL.md:397
Finding
Shell Injection Risk in Generated AWS Remediation Script## Vulnerability Details **File Location**: `SKILL.md`, lines 397-416 **Vulnerability Type**: Unsafe construction of executable shell commands from cloud resource identifiers **Risk Level**: Medium ### Vulnerable Code ```python for resource in data.get('ResourceTagMappingList', []): arn = resource['ResourceARN'] tags = {t['Key']: t['Value'] for t in resource.get('Tags', [])} missing = {k: v for k, v in required.items() if k not in tags} if missing: tags_json = json.dumps(missing) commands.append(f'aws resourcegroupstaggingapi tag-resources --resource-arn-list \"{arn}\" --tags \'{tags_json}\'') print('#!/bin/bash') print('# Auto-generated tagging remediation script') print(f'# {len(commands)} resources to remediate') print('# Review before running -- placeholder values (NEEDS-*) must be replaced by owners') print('set -euo pipefail') print() for i, cmd in enumerate(commands): print(f'echo \"[{i+1}/{len(commands)}] Tagging resource...\"') print(cmd) print('sleep 0.2 # rate limiting') print() ``` ### Technical Analysis The remediation generator treats `ResourceARN`, obtained from the cloud API response, as trusted text and concatenates it directly into an executable shell command: ```python commands.append( f'aws resourcegroupstaggingapi tag-resources ' f'--resource-arn-list \"{arn}\" --tags \'{tags_json}\'' ) ``` Double quotes do not neutralize all shell syntax. In particular, command substitutions such as `$()` and backticks remain active inside double-quoted strings. If a supported cloud resource identifier can contain shell-significant characters, or if the API input is otherwise manipulated, the generated script can execute unintended commands when an operator runs it. The generated tag values are currently fixed placeholders, which limits that specific input vector. The principal unsafe value in the documented implementation is ...[truncated 1516 chars]
Remediation
## Remediation Suggestions Avoid generating executable shell source from cloud-derived values. Prefer a Python remediation program that invokes the AWS CLI with an argument array: ```python import json import subprocess subprocess.run( [ "aws", "resourcegroupstaggingapi", "tag-resources", "--resource-arn-list", arn, "--tags", json.dumps(missing), ], check=True, shell=False, ) ``` If a Bash script must be generated, quote every dynamic argument with `shlex.quote`: ```python import json import shlex command = [ "aws", "resourcegroupstaggingapi", "tag-resources", "--resource-arn-list", arn, "--tags", json.dumps(missing), ] print(" ".join(shlex.quote(argument) for argument in command)) ``` The remediation workflow should also validate ARNs against expected service-specific formats, write generated scripts with restrictive permissions, require explicit operator approval, and execute them in a least-privileged environment without unrelated credentials.
Vulnerability Patterns
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Findings (2)

Vague Triggers

Medium
Confidence
90% confidence
Finding
The trigger list includes very broad phrases such as "missing tags," "enforce tags," and "tagging policy," which can match ordinary cloud-support conversations and cause the skill to activate unexpectedly. Because this skill can progress from auditing into generating remediation commands for live cloud environments, overbroad activation increases the chance of invoking infrastructure-changing guidance in contexts where the user did not clearly request it.

Missing User Warnings

Medium
Confidence
95% confidence
Finding
The skill generates executable remediation scripts that tag live AWS, GCP, and potentially other cloud resources, including placeholder values like NEEDS-OWNER and CC-0000, without an upfront safety warning or mandatory approval checkpoint. In a real environment, running these scripts can mutate production infrastructure metadata at scale, corrupt governance data, interfere with billing/compliance processes, and make later attribution harder by bulk-applying incorrect tags.

Static analysis

No suspicious patterns detected.