calorie1-detective-v3

• Outcome: Pass
• Latest audit: May 29, 2026, 8:13 PM
• Version:
  3.0.2

Overview

This is a coherent food-photo calorie calculator, but users should understand that selected images are sent to Kimi/Moonshot for recognition.

Install only if you are comfortable sending chosen food photos to Kimi/Moonshot for processing. Avoid images containing faces, documents, location clues, or private background details; keep API keys in environment variables or a private local config; and install dependencies in an isolated environment with current patched package versions.

SkillSpector

By NVIDIA

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• MCP Least Privilege: Underdeclared Capability, Wildcard Permission, Missing Permission Declaration
• MCP Tool Poisoning: Hidden Instructions, Unicode Deception, Parameter Description Injection
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands

Findings (12)

Lp3

Medium

• Category: MCP Least Privilege
• Confidence: 88% confidence
• Finding: The skill advertises capabilities that imply environment access, file reading, and network use, but it does not declare any permissions in the manifest. This creates a transparency and policy-enforcement gap: users and the hosting platform cannot accurately assess or constrain what the skill may access, increasing the risk of unintended data exposure or outbound requests.

Intent-Code Divergence

Low

• Confidence: 81% confidence
• Finding: The front matter identifies the skill as v2.0.0 while the body presents it as v3.0.0 with newer functionality and release history. This inconsistency can mislead reviewers and users about the actual code and feature set being deployed, which undermines trust, complicates auditing, and can conceal unreviewed changes.

Missing User Warnings

Medium

• Confidence: 93% confidence
• Finding: The code base64-encodes the entire local image and sends it to Moonshot's external API for vision recognition, but the user-facing flow and report do not clearly warn that the photo leaves the local environment. This creates a privacy and data-handling risk because food photos may contain sensitive metadata or incidental personal content, and users may not realize they are transmitting images to a third party.

External Transmission

Medium

• Category: Data Exfiltration

Content

}
        
        try:
            response = requests.post(
                "https://api.moonshot.cn/v1/chat/completions",
                headers=headers,
                json=payload,

• Confidence: 91% confidence
• Finding: requests.post( "https://

External Transmission

Medium

• Category: Data Exfiltration

Content

}
        
        try:
            response = requests.post(
                "https://api.moonshot.cn/v1/chat/completions",
                headers=headers,
                json=payload,

• Confidence: 91% confidence
• Finding: requests.post( "https://api.moonshot.cn/v1/chat/completions", headers=headers, json=

External Transmission

Medium

• Category: Data Exfiltration

Content

try:
            response = requests.post(
                "https://api.moonshot.cn/v1/chat/completions",
                headers=headers,
                json=payload,
                timeout=60

• Confidence: 90% confidence
• Finding: https://api.moonshot.cn/

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# Food Calorie Calculator Dependencies
requests>=2.28.0
pyyaml>=6.0
Pillow>=9.0.0

• Confidence: 92% confidence
• Finding: requests>=2.28.0

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# Food Calorie Calculator Dependencies
requests>=2.28.0
pyyaml>=6.0
Pillow>=9.0.0

• Confidence: 95% confidence
• Finding: pyyaml>=6.0

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# Food Calorie Calculator Dependencies
requests>=2.28.0
pyyaml>=6.0
Pillow>=9.0.0

• Confidence: 94% confidence
• Finding: Pillow>=9.0.0

Known Vulnerable Dependency: requests — 10 advisory(ies): CVE-2014-1830 (Exposure of Sensitive Information to an Unauthorized Actor in Requests); CVE-2024-47081 (Requests vulnerable to .netrc credentials leak via malicious URLs); CVE-2024-35195 (Requests `Session` object does not verify requests after making first request wi) +7 more

High

• Category: Supply Chain
• Confidence: 83% confidence
• Finding: requests

Known Vulnerable Dependency: pyyaml — 8 advisory(ies): CVE-2019-20477 (Deserialization of Untrusted Data in PyYAML); CVE-2020-1747 (Improper Input Validation in PyYAML); CVE-2020-14343 (Improper Input Validation in PyYAML) +5 more

Critical

• Category: Supply Chain
• Confidence: 90% confidence
• Finding: pyyaml

Known Vulnerable Dependency: Pillow — 10 advisory(ies): CVE-2016-2533 (Pillow buffer overflow in ImagingPcdDecode); CVE-2023-50447 (Arbitrary Code Execution in Pillow); CVE-2021-27922 (Pillow Uncontrolled Resource Consumption) +7 more

Critical

• Category: Supply Chain
• Confidence: 94% confidence
• Finding: Pillow

VirusTotal

65/65 vendors flagged this skill as clean.