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Security audit

NeuroSync FPS Calibrator

Security checks for vulnerabilities and agentic risk

Overview

The skill appears to be a game sensitivity calibrator, but its code can return calibration results unrelated to the user's submitted data and makes overconfident privacy claims.

Review before installing or paying for use. Do not rely on the calibration output until the submitted angles are actually passed into the core calculation, fixed fallback constants are removed, video-processing failures return errors, privacy masking limits are documented, dependency versions are pinned, and the child script path is resolved from the trusted package directory.

A.I.G

Vulnerability Patterns
  • Tool Hijacking and SpoofingModifies or replaces tools so legitimate-looking calls execute attacker logic
  • 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
Findings (3)

T07 · Tool Hijacking and Spoofing

Error
Location
kiwi_bridge.py:42
Finding

Relative Child-Script Resolution Enables Local Code Substitution

Content
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Remediation
View remediation

T09 · Insecure Skill Coding Practices

Error
Location
kiwi_bridge.py:25
Finding

User Input Is Discarded and Successful Calibration Values Can Be Fabricated

Content
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Remediation
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T08 · Insecure Dependencies

Warning
Location
manifest.json:8
Finding

Unpinned Third-Party Dependencies Create Supply-Chain and Reproducibility Risk

Content
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Remediation
View remediation

SkillSpector

Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Behavioral ASTexec() Call, eval() Call, Dynamic Import
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (11)

Env Variable Harvesting

High
Category
Data Exfiltration
Confidence
60% confidence
Finding

Code enumerates, copies, or searches environment variables for secrets. Bulk environment access can collect credentials unrelated to the skill's stated purpose.

Content

Scanner excerpt · kiwi_bridge.py (reported line 34)May include surrounding context.

python
# 透過複製當前系統環境變數，並強制加上 PYTHONIOENCODING = utf-8，
        # 這樣一來，你的 neurosync_calibration.py 就能順利印出 'R²' 與繁體中文，絕對不會再噴發編碼錯誤！
        # =============================================================
        env = os.environ.copy()
        env["PYTHONIOENCODING"] = "utf-8"

        # 2. 【核心外包呼叫】：啟動子進程呼叫原始碼，不改動任何一行二值化閾值

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The entire skill description and operating instructions are written only in Traditional Chinese, with no indication that other languages are supported or that the user may choose a preferred language. Under the stated policy, forcing a specific language without user opt-in is a natural-language policy violation.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

This code emits user-facing text in Traditional Chinese, and subsequent exception and status messages are also written only in Chinese. The file does not offer a language choice or document a justified region-specific constraint, which creates a language/locale policy issue under the natural-language policy rule.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

The comment at L37 says this is a core delegated call that invokes the original code without changing threshold logic, implying the subprocess will process the input data validated above. In reality, the subprocess is launched with no stdin or arguments carrying real_angles, so the earlier input handling is disconnected from the actual computation performed by neurosync_calibration.py.

Content

No source excerpt is available for this finding.

subprocess module call

Medium
Category
Dangerous Code Execution
Confidence
70% confidence
Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · kiwi_bridge.py (reported line 38)May include surrounding context.

python
env["PYTHONIOENCODING"] = "utf-8"

        # 2. 【核心外包呼叫】：啟動子進程呼叫原始碼，不改動任何一行二值化閾值
        process = subprocess.Popen(
            [sys.executable, "neurosync_calibration.py"],
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,

Ae4

Medium
Category
analysis-evasion
Confidence
80% confidence
Finding

Suspicious Unicode normalization or mixed-script content

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

This code contains extensive natural-language output in Traditional Chinese, beginning in the docstring and printed headers, and the same pattern continues throughout the file. Under the policy, forcing a specific language without user opt-in is a natural-language locale violation unless the locale restriction is clearly justified.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
85% confidence
Finding

The comment at L250-L252 states the report 'forces' K_sys = 1.0 and K_accel = 0.003, implying a consistent fixed-calibration analysis. However, the subsequent calculations only hard-code those constants in formulas while still pulling S and A from S_parameters/A_parameters arrays, and the earlier fitted values returned by fit_universal_model are ignored entirely. This makes the intent of the report misleading relative to what is actually being analyzed.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The natural-language description, inputs, outputs, and safety statement are presented in Chinese, but the file does not indicate that the user can choose another language or that the skill is intentionally limited to a Chinese-speaking audience. This can violate language or locale policy when users are not given an opt-in choice.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The skill makes a strong security/privacy assurance that every video frame is automatically masked, but the documented interface only accepts real_angles and exposes no actual video-ingestion or masking mechanism. False security claims can cause users to trust the tool with sensitive gameplay recordings or account-linked data under incorrect assumptions, creating privacy and compliance risk.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
97% confidence
Finding

The manifest's primary user-facing description is entirely in Traditional Chinese, with no indication that the skill supports other languages or that this locale restriction is intentional and region-specific. Under the policy, forcing a specific language without user opt-in can be a natural-language policy violation.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.