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Security audit

Ai Humanizer.Disabled

Security checks for vulnerabilities and agentic risk

Overview

This skill is a coherent writing-analysis and rewriting tool, with some deployment and usage cautions but no evidence of hidden exfiltration, destructive behavior, or deception.

Install it if you want a local writing-style checker, but use autofix only after reviewing the rewritten text. Do not make its always-on prompt snippets global for legal, academic, compliance, evidence, or exact-quotation work. If you run the HTTP API beyond localhost, add request-size limits, authentication, rate limiting, and clear handling rules for sensitive drafts.

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 (1)

T09 · Insecure Skill Coding Practices

Warning
Location
api-server/server.js:36
Finding

Unbounded HTTP Request Body Enables Remote Denial of Service

Content
View full analysis

Vulnerability Details

File Location: api-server/server.js:36-50
Vulnerability Type: Uncontrolled resource consumption through unbounded request buffering
Risk Level: Medium
Classification: T09: Insecure Skill Coding Practices

Vulnerable Code

js
// Parse JSON body
async function parseBody(req) {
  return new Promise((resolve, reject) => {
    let body = '';
    req.on('data', chunk => body += chunk);
    req.on('end', () => {
      try {
        resolve(body ? JSON.parse(body) : {});
      } catch (e) {
        reject(new Error('Invalid JSON'));
      }
    });
    req.on('error', reject);
  });
}

The unbounded parser is invoked for every POST request at api-server/server.js:95-96:

js
// POST endpoints
if (req.method === 'POST') {
  const body = await parseBody(req);

Technical Analysis

parseBody() appends every incoming chunk to an in-memory string without enforcing a maximum body size. It also does not implement a request timeout or abort processing when a client sends data too slowly.

An attacker who can reach the HTTP server can therefore submit an arbitrarily large JSON request. The server retains the request content in memory, creates additional allocations while concatenating strings, and then passes the body to JSON.parse(). For valid requests, the supplied text is subsequently processed by synchronous pattern-matching and statistical-analysis routines, increasing CPU and memory pressure.

The server starts with server.listen(PORT) and does not explicitly restrict itself to a loopback address. Exposure depends on the runtime environment and network controls, but the code itself does not enforce local-only access. No authentication or rate limiting mitigates repeated requests.

Attack Path

  1. The attacker obtains network access to the configured API port.
  2. The attacker opens one or more POST requests to an API endpoint such as /api/analyze.
  3. The attacker streams an oversized JSON body o ...[truncated 905 chars]
Remediation
View remediation

Remediation Suggestions

  1. Enforce a strict request-body limit. Track received bytes rather than JavaScript string length. Reject oversized requests with HTTP 413 Payload Too Large and destroy the request stream.
js
const MAX_BODY_BYTES = 1_000_000;

async function parseBody(req) {
  return new Promise((resolve, reject) => {
    const chunks = [];
    let received = 0;
    let settled = false;

    req.on('data', (chunk) => {
      received += chunk.length;

      if (received > MAX_BODY_BYTES) {
        settled = true;
        const error = new Error('Request body too large');
        error.statusCode = 413;
        req.destroy(error);
        reject(error);
        return;
      }

      chunks.push(chunk);
    });

    req.on('end', () => {
      if (settled) return;

      try {
        const body = Buffer.concat(chunks).toString('utf8');
        resolve(body ? JSON.parse(body) : {});
      } catch {
        const error = new Error('Invalid JSON');
        error.statusCode = 400;
        reject(error);
      }
    });

    req.on('error', (error) => {
      if (!settled) reject(error);
    });
  });
}
  1. Return appropriate status codes. Map oversized bodies to HTTP 413 and malformed JSON to HTTP 400 rather than exposing all parser failures as HTTP 500.

  2. Validate the parsed input. Require body.text to be a string and enforce a separate maximum text length before invoking analysis routines.

  3. Configure timeouts. Set server.requestTimeout, server.headersTimeout, and server.keepAliveTimeout to bounded values to reduce slow-request attacks.

  4. Restrict default network exposure. Bind to 127.0.0.1 by default and require explicit configuration to listen on external interfaces:

js
const HOST = process.env.HOST || '127.0.0.1';
server.listen(PORT, HOST);
  1. Protect public deployments. Place the server behind a reverse proxy or gateway that provides authentication, per-client rate limiting, connection limits, a ...[truncated 220 chars]
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (36)

Tp4

High
Category
MCP Tool Poisoning
Confidence
99% confidence
Finding

The supplied code chunk is purely an ESLint config. It sets linting presets, file globs, language options, globals, style/error-prevention rules, and ignored directories. There is nothing in this file that processes natural language text, detects LLM-style patterns, scores writing, or rewrites content. The actual behavior is materially different from the declared purpose, so this is a clear mismatch.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
98% confidence
Finding

The declared description says the skill 'humanizes' AI-generated text by removing patterns and rewriting content to sound natural. In this code chunk, the implemented functions are limited to analysis workflows: collecting text files, reading them from disk, scoring them via an external analyzer, aggregating hotspots, and comparing before/after analysis results. The primary purpose shown here is detection/scoring/reporting over files and drafts, not transformation of text. The code also accesses the filesystem recursively, an undeclared capability/resource access. While the description does mention reviewing/scoring text for AI patterns, the absence of any rewriting behavior and the addition of repo/file scanning make this a material description-behavior mismatch.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
99% confidence
Finding

The declared description describes a full text-processing capability for detecting and rewriting AI-generated prose. The actual code chunk only configures the Vitest test framework by setting globals to true and specifying which test files to include. This is materially unrelated to the declared purpose and does not evidence any of the claimed humanization, detection, statistical analysis, or vocabulary-tier features.

Content

No source excerpt is available for this finding.

Ae1

High
Category
analysis-evasion
Confidence
100% confidence
Finding

Referenced artifact was not completely inspected

Content

Scanner excerpt · SKILL.md (reported line 115)May include surrounding context.

md
echo "Your text here" | node src/cli.js score

Ae1

High
Category
analysis-evasion
Confidence
100% confidence
Finding

Referenced artifact was not completely inspected

Content

Scanner excerpt · SKILL.md (reported line 118)May include surrounding context.

md
echo "Your text here" | node src/cli.js score

Ae1

High
Category
analysis-evasion
Confidence
100% confidence
Finding

Referenced artifact was not completely inspected

Content

Scanner excerpt · SKILL.md (reported line 121)May include surrounding context.

md
echo "Your text here" | node src/cli.js score

Ae1

High
Category
analysis-evasion
Confidence
100% confidence
Finding

Referenced artifact was not completely inspected

Content

Scanner excerpt · SKILL.md (reported line 124)May include surrounding context.

md
echo "Your text here" | node src/cli.js score

Ae1

High
Category
analysis-evasion
Confidence
100% confidence
Finding

Referenced artifact was not completely inspected

Content

Scanner excerpt · SKILL.md (reported line 127)May include surrounding context.

md
echo "Your text here" | node src/cli.js score

Ae1

High
Category
analysis-evasion
Confidence
100% confidence
Finding

Referenced artifact was not completely inspected

Content

Scanner excerpt · SKILL.md (reported line 130)May include surrounding context.

md
echo "Your text here" | node src/cli.js score

Ae1

High
Category
analysis-evasion
Confidence
100% confidence
Finding

Referenced artifact was not completely inspected

Content

Scanner excerpt · SKILL.md (reported line 133)May include surrounding context.

md
echo "Your text here" | node src/cli.js score

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
83% confidence
Finding

The markdown advertises 'Humanize with auto-fixes' and later 'Apply safe auto-fixes' for user-supplied files, which implies file/content modification. The README does not include a clear user warning to review outputs carefully or that automatic rewriting may alter meaning, style, or original wording.

Content

No source excerpt is available for this finding.

Undeclared Tool Scope

Medium
Category
MCP Least Privilege
Confidence
70% confidence
Finding

Without declared permissions the skill's intent is opaque and cannot be validated.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The trigger language is very broad and overlaps with ordinary writing, editing, and review requests, which can cause the skill to activate in situations beyond explicit user intent. Overbroad activation increases the chance of unsolicited transformation of user content, including safety-relevant text, policy text, academic writing, or evidence-like material where preserving wording matters.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

The 'Always-on mode' guidance instructs agents to bake these transformation rules into their core personality or system prompt, making the behavior persistent and difficult for downstream users to scope or disable. That can silently alter outputs across unrelated tasks, suppress normal assistant disclosures/style, and interfere with trustworthy rendering of sensitive or high-integrity content.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The API is explicitly designed to accept arbitrary user text for scoring, analysis, and rewriting, and the OpenAPI spec points to a remote server endpoint without clearly warning users that submitted content leaves the local environment. That creates a real privacy and data-handling risk because users may submit sensitive drafts, proprietary text, or personal data under the assumption the processing is local or agent-contained.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

This code exposes POST API endpoints that receive user text over HTTP and process it server-side, which is a network/data-transmission operation covered by the warning requirement for code files. While the file documents the endpoints, it does not include any user disclosure about submitted content being sent to the server or any privacy-related notice in comments or logs near the operation.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
83% confidence
Finding

The text explicitly states that current checks are 'English-first' and that non-English handling needs improvement. This can amount to a natural-language policy issue because the skill behavior appears biased toward a specific language without documenting user choice or a justified region/language limitation.

Content

No source excerpt is available for this finding.

Rp1

Medium
Category
MCP Rug Pull
Confidence
70% confidence
Finding

npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.

Content

No source excerpt is available for this finding.

External Transmission

Medium
Category
Data Exfiltration
Confidence
60% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · docs/INTEGRATIONS.md (reported line 102)May include surrounding context.

API example

bash
curl -X POST http://localhost:3000/api/score \
  -H "Content-Type: application/json" \
  -d '{"text": "This draft needs tighter wording and more specifics."}'

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The code presents deterministic labels such as 'heavily AI-generated' and summary text like 'The text looks human-written' without prominently communicating uncertainty, error rates, or false-positive risk. In a skill explicitly designed to judge whether text appears AI-written, users may over-trust these outputs and make consequential moderation, academic, hiring, or trust decisions based on probabilistic heuristics presented as firm conclusions.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
85% confidence
Finding

The string on L327 directs users to add first-person phrasing such as "I found" and "We noticed" as a general humanization tactic. This is a natural-language policy concern because it imposes a specific voice/style choice on generated writing rather than offering it as optional or context-dependent.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
88% confidence
Finding

The comment and implementation explicitly define syllable counting as an English heuristic by stripping non-[a-z] characters and applying English vowel rules. This creates a language-specific constraint in a general text-statistics engine without any user choice or clear region-specific justification, which matches the language/locale policy concern.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
88% confidence
Finding

The test suite repeatedly validates a 'humanization' engine that rewrites text toward a fixed style, including replacing specific phrases and removing conversational expressions, which indicates the skill enforces a particular language style policy. In the reviewed file there is no indication that users can opt in to this style normalization or choose alternatives, which matches the policy-violation category for language or locale constraints.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
72% confidence
Finding

The README encourages scanning an entire docs folder, which may process large sets of potentially sensitive text files. There is no nearby disclosure reminding users to avoid scanning confidential content unintentionally or to verify the scope before running recursive analysis.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Low
Category
Not specified by scanner
Confidence
96% confidence
Finding

The banner states '24 patterns. 5 categories. Zero dependencies,' while the provided manifest describes '28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis' over multiple categories. This is an active documentation contradiction about the skill's implemented scope and characteristics, not merely an omission.

Content

No source excerpt is available for this finding.

Static analysis

Detected: suspicious.prompt_injection_instructions

Prompt-injection style instruction pattern detected.

Warn
Code
suspicious.prompt_injection_instructions
Location
docs/INTEGRATIONS.md:123