Back to skill

Security audit

Essay Humanize Iterator

Security checks for vulnerabilities and agentic risk

Overview

This skill is openly designed to make essays appear less AI-generated and uses shell-based measurement in an under-scoped way, so it needs review before installation.

Review this carefully before installing. Use it only for transparent editing, not to conceal AI assistance or bypass academic or platform review. If you do install it, run it in an isolated environment, pin the spaCy package and model, and require essay text to be passed through a file, stdin, or a shell-free argument API rather than interpolated into a command string.

Vulnerability Patterns
  • Insecure DependenciesIntroduces malicious components through unsafe dependency sources
  • 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
Findings (2)

T09 · Insecure Skill Coding Practices

Error
Location
skill.yaml:37
Finding

Shell Command Injection Through Untrusted Essay Interpolation

Content
View full analysis
threshold or metrics outside human range, run rewrite with targeted feedback 4. Re-measure after rewrite 5. Repeat until passing or max iterations reached 6. Output final essay + iteration report table ``` ### Technical Analysis The Skill requests the `shell` permission and directs the orchestrating agent to substitute user-controlled essay text into a quoted command-line argument: ```bash python skill/scripts/measure.py --text "ESSAY" ``` Shell quoting does not safely neutralize arbitrary untrusted content. An essay containing a double quote followed by shell syntax can terminate the intended argument and introduce another command. Command substitutions may also be evaluated inside double quotes by common shells. The Python script itself uses `argparse` safely, but that does not protect the command-construction layer. Exploitation occurs before Python receives the arguments. The vulnerability applies when the orchestrator follows the natural-language instruction by constructing a shell command as written rather than using an argument-array API. ### Attack Path 1. An attacker submits an essay containing shell metacharacters, quote termination, or command-substitution syntax. 2. The Skill activates because the user requests essay measurement or humanization. 3. The shell-enabled orchestrator substitutes the essay into the documented `--text "ESSAY"` command. 4. The shell interprets the injected syntax before invoking `measure.py`. 5. The injected command runs with the same operating-system privileges and filesystem access as the agent process. A conceptual malicious input could terminate the quoted argument, append another shell command, and comment out ...[truncated 809 chars]
Remediation
View remediation

T08 · Insecure Dependencies

Warning
Location
requirements.txt:1
Finding

Unpinned Third-Party Dependency and Model Installation

Content
View full analysis
=3.7.0 ``` Related installation instructions in `README.md`: ```markdown - spaCy with `en_core_web_sm` model (must be installed manually before first use) ```bash pip install -r requirements.txt python -m spacy download en_core_web_sm ``` ``` ### Technical Analysis The requirement specifies only a minimum spaCy version and permits the package resolver to install any future compatible release. No lockfile or cryptographic hashes constrain the selected spaCy package or its transitive dependencies. The separately downloaded `en_core_web_sm` model is also not pinned to a reviewed version or verified by a project-supplied hash. Consequently, installations performed at different times can resolve to different executable artifacts. Python package installation may execute package build or installation logic. Loading spaCy and its model also imports executable Python components. Therefore, dependency integrity is part of the Skill's trusted computing base even though the project does not directly contain malicious dependency code. This finding is a supply-chain hardening issue. The audited repository does not demonstrate that the current official spaCy package or model is malicious. ### Attack Path 1. A user follows the documented installation commands. 2. The package resolver selects the newest release satisfying `spacy>=3.7.0` and resolves its transitive dependencies. 3. The model download command obtains a compatible model selected at installation time. 4. If a future selected artifact, transitive dependency, package index response, or model distribution is compromised, its installation or imported code executes locally. 5. The compromised component receives the privileges available to the Python environme ...[truncated 730 chars]
Remediation
View remediation
Vulnerability Patterns
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (31)

Natural-Language Policy Violations

High
Category
Not specified by scanner
Confidence
97% confidence
Finding

The skill is explicitly designed to iteratively rewrite essays to reduce detection by AI-writing detectors, which is an evasion capability rather than a neutral writing aid. In an academic context this facilitates policy circumvention, deception, and misuse of LLM-generated content while making enforcement and attribution harder.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

High
Category
Not specified by scanner
Confidence
98% confidence
Finding

The description explicitly states the skill's purpose is to reduce false positives from AI detectors by removing stereotypical AI patterns and aligning output with human baselines. This is dangerous because it operationalizes evasion of authorship-detection controls, which can facilitate academic dishonesty, policy circumvention, and deceptive provenance of content.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

High
Category
Not specified by scanner
Confidence
98% confidence
Finding

The entry-point instructions directly tell the agent to activate for requests to rehumanize text and reduce AI stylistic patterns, making evasion behavior a core, deliberate function rather than an incidental side effect. This materially increases misuse risk because the trigger logic is tuned to catch and serve detector-avoidance requests.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

High
Category
Not specified by scanner
Confidence
99% confidence
Finding

This section explicitly instructs iterative rewriting to lower an 'AI score' by removing detector-linked linguistic features, which is guidance for evading AI-origin detection rather than improving content quality. In a skill context, this can be operationalized to systematically conceal machine-generated text and bypass academic, platform, or compliance controls.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

High
Category
Not specified by scanner
Confidence
99% confidence
Finding

The convergence criteria define success as reaching numeric thresholds associated with 'human baselines,' which turns the process into an optimization loop for detector evasion. This makes the skill more dangerous because it provides a stopping condition for adversarial rewriting until text is statistically less likely to be flagged as AI-generated.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

High
Category
Not specified by scanner
Confidence
98% confidence
Finding

This section describes dynamically generating system prompts from measurement output to enforce rewrite constraints, effectively creating an automated feedback loop for anti-detection text transformation. Automating prompt construction from detector-style signals materially increases misuse potential by making evasion repeatable and scalable.

Content

No source excerpt is available for this finding.

Ssd 2

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The opening description states that the purpose is to refine essays so they avoid false positives from AI detectors by matching human baselines and removing stereotypical AI patterns. That is a direct content-evasion objective, and the academic-essay context makes it especially dangerous because it can be used to conceal AI authorship and defeat institutional safeguards.

Content

No source excerpt is available for this finding.

Ssd 2

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The trigger phrases operationalize the evasion behavior by instructing users to 'humanize' essays and reduce AI-style patterns. This lowers the barrier to misuse by turning detector-avoidance into a one-shot user workflow, making abuse easier and more scalable.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
83% confidence
Finding

The README makes a strong privacy/security claim that all rewriting happens locally with no external API calls, yet the documented OpenClaw/ClawHub workflow implies LLM orchestration that may occur through a hosted service. Misrepresenting data flow can cause users to submit sensitive essays or academic work under false assumptions about confidentiality and processing boundaries.

Content

No source excerpt is available for this finding.

Ssd 2

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The skill is explicitly designed to rewrite text so it avoids AI-detector heuristics by reducing detectable stylistic signals. That makes the core function an evasion mechanism for authenticity or policy enforcement systems, enabling users to disguise AI-generated or heavily machine-assisted work as human-authored.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
79% confidence
Finding

The trigger list explicitly includes Chinese phrases alongside English ones, but the specification does not state that the skill is multilingual by user choice or limited to a Chinese-speaking context. Under the policy rule, language or locale behavior should be opt-in or clearly justified rather than implicitly embedded.

Content

No source excerpt is available for this finding.

Ssd 4

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The workflow creates a closed feedback loop of measure, rewrite, and repeat until detector thresholds are passed. This materially increases the effectiveness of evasion by systematically optimizing text against the target metrics, making abuse much easier and more reliable than one-off paraphrasing.

Content

No source excerpt is available for this finding.

Ssd 2

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The iteration strategy specifically instructs the system to remove weighted AI patterns, vary syntax, and tune semantic density to suppress signals used by detection systems. In context, these are not neutral editing guidelines; they are targeted countermeasures against detection, which can facilitate academic dishonesty, fraud, and policy circumvention.

Content

No source excerpt is available for this finding.

Ssd 2

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The skill is framed as an iterative paraphrasing and refinement system for minimizing detectable AI-writing traces, which makes concealment of content origin a stated objective. Even without direct exploitation of a host system, this enables abuse by helping users evade integrity and moderation mechanisms.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

Medium
Category
Not specified by scanner
Confidence
90% confidence
Finding

The skill requests shell access even though its primary function is text rewriting and scoring, which expands the attack surface beyond what is necessary. Because the prompt explicitly instructs the agent to run a local Python script on user-supplied essay text, shell capability could be abused if command construction is unsafe elsewhere in the skill implementation or supporting files.

Content

No source excerpt is available for this finding.

Ssd 2

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The workflow measures AI-pattern scores, rewrites toward passing a threshold, and repeats until the text appears more human-like, which is a clear optimization loop for detector evasion. The iterative, metrics-driven nature makes the capability more effective and therefore more dangerous than ordinary style editing.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

The activation guidance includes broad triggers such as improving writing naturalness or essay quality, which can cause the skill to activate in benign writing-help contexts where detector-evasion behavior was not requested. That increases the chance of unintended misuse and can route ordinary user content into a workflow designed to conceal AI authorship characteristics.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The keyword list contains generic writing-related phrases that are likely to collide with common editing requests, causing overbroad invocation. In this skill's context, accidental activation is more dangerous because the core behavior is to iteratively reduce detectable AI-writing signals.

Content

No source excerpt is available for this finding.

Ssd 2

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The skill is explicitly designed to rewrite text to reduce AI-detector signals and remove identifiable AI-writing patterns. That is dangerous because it operationalizes evasion of provenance and authenticity checks, enabling academic dishonesty, policy circumvention, and deceptive submission of machine-generated content as human-authored work.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

The activation examples include broad phrases such as 'improve writing naturalness' and requests to improve 'semantic density' or 'syntactic complexity', which could match ordinary editing or tutoring requests rather than this specific skill. The spec does not provide exclusion conditions or negative examples to clarify when the skill should not activate.

Content

No source excerpt is available for this finding.

Ssd 4

Medium
Category
Not specified by scanner
Confidence
99% confidence
Finding

The workflow establishes a closed-loop rewrite-and-measure process that repeatedly tests against detector-oriented metrics until the text passes thresholds. This makes the skill more dangerous than simple editing because it systematizes iterative evasion, improving the user's ability to defeat heuristic screening at scale and with measurable feedback.

Content

No source excerpt is available for this finding.

Ssd 4

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The staged iteration strategy explicitly prioritizes removing AI markers and then fine-tuning the text to appear more human over multiple passes. In context, this is not neutral style guidance; it is a structured escalation plan for concealment, which materially increases the effectiveness of evading detector-based safeguards.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The metrics specify computation via spaCy dependency parsing with the en_core_web_sm model, which restricts the skill to English-language analysis. This is a natural-language locale constraint, but the file does not indicate user choice, opt-in, or a documented justification for enforcing English only.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The module and function documentation describe a repeated measure -> rewrite -> re-measure loop, implying multiple iterations up to max_iters. However, on the first non-passing report, the code builds feedback and immediately returns at L147-L153, so no second or later iteration ever occurs within this function.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The argparse description and surrounding module text present the tool as an iterative humanization engine. In practice, the CLI calls iterate_humanize once, and that function either returns immediately on pass or returns pending feedback on first failure; it never performs a rewrite or a true multi-step iteration.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.