Product Image Archiver

• Outcome: Review
• Latest audit: May 28, 2026 · 11:12 AM UTC
• Version: 1.0.1

Overview

The skill is a manual product-image archiver, but it overstates legal/risk checks and can report unverified compliance-style results as if they were validated.

Install only if you treat the generated infringement, license, CMA, and supplier-quality information as unverified placeholders, not compliance evidence. Use trusted product links, review downloaded images manually before business or legal decisions, and pin/audit dependencies before using this in a production workflow.

SkillSpector

Vulnerability Patterns

• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• MCP Tool Poisoning: Hidden Instructions, Unicode Deception, Parameter Description Injection
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access

Findings (13)

Description-Behavior Mismatch

Medium

• Confidence: 96% confidence
• Finding: The code hard-codes infringement/compliance-related fields such as cma_report, image_license, logo_detected, and portrait_detected instead of deriving them from actual analysis or verification. In this skill’s context—claiming '侵权风险标注' and '完整性校验' for archived product images—fabricated safety/compliance metadata can mislead operators into trusting unverified content, creating legal, compliance, and downstream decision-making risk.

Intent-Code Divergence

Medium

• Confidence: 90% confidence
• Finding: The adapter populates compliance- and trust-related fields such as cma_report, image_license, one_piece_drop, and supplier performance metrics with hard-coded values rather than data derived from the source page or a verified backend. In this skill context, those fields could be consumed downstream to make archival, infringement-risk, or sourcing decisions, causing users to trust fabricated compliance signals and potentially mishandle copyrighted or non-compliant product images.

Description-Behavior Mismatch

Medium

• Confidence: 82% confidence
• Finding: The implementation only fetches a product page and extracts metadata plus image URLs, but the skill advertises lossless original-image download, infringement-risk marking, and integrity verification. This mismatch is dangerous because downstream users or automation may assume images were actually archived and verified when they were not, leading to false assurance, broken evidence chains, or improper compliance handling.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# 版本：1.0.0

# HTTP 请求
requests>=2.31.0
urllib3>=2.0.0

# 数据处理

• Confidence: 89% confidence
• Finding: Using requests>=2.31.0 allows future versions to be installed without review, which can introduce breaking changes or newly vulnerable releases into the environment. In a network-facing image archiving skill, dependency drift increases supply-chain and operational risk because HTTP handling is central to the workflow.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# HTTP 请求
requests>=2.31.0
urllib3>=2.0.0

# 数据处理
pandas>=2.0.0

• Confidence: 89% confidence
• Finding: Using urllib3>=2.0.0 leaves the installed version open-ended, which can pull in unreviewed releases and make builds non-reproducible. Because this skill appears to download remote images, the HTTP transport library is security-relevant and dependency drift raises exposure to future regressions or CVEs.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

urllib3>=2.0.0

# 数据处理
pandas>=2.0.0
openpyxl>=3.1.0

# 图像处理

• Confidence: 80% confidence
• Finding: pandas>=2.0.0 is not reproducible and can introduce unexpected behavior or vulnerable versions over time. While pandas is less exposed than the network stack here, uncontrolled upgrades still create supply-chain risk.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# 数据处理
pandas>=2.0.0
openpyxl>=3.1.0

# 图像处理
Pillow>=10.0.0

• Confidence: 84% confidence
• Finding: openpyxl>=3.1.0 permits unbounded upgrades, which is risky for a package that parses spreadsheet files and has had XML-related security issues historically. If this skill imports or exports Excel metadata, an unsafe future version could affect file parsing security.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

openpyxl>=3.1.0

# 图像处理
Pillow>=10.0.0

# 环境变量加载
python-dotenv>=1.0.0

• Confidence: 92% confidence
• Finding: Pillow>=10.0.0 is especially risky because image parsing libraries often have memory corruption and resource-exhaustion flaws, and this skill's core function is handling product images. Unpinned image-processing dependencies increase the chance of silently adopting a vulnerable release in a high-exposure code path.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

Pillow>=10.0.0

# 环境变量加载
python-dotenv>=1.0.0

# 日志
colorlog>=6.7.0

• Confidence: 75% confidence
• Finding: python-dotenv>=1.0.0 allows non-reproducible installs and may pull in future vulnerable versions. The risk is lower than for network or image libraries, but environment-variable tooling can still affect secrets handling and deployment behavior.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

python-dotenv>=1.0.0

# 日志
colorlog>=6.7.0

• Confidence: 72% confidence
• Finding: colorlog>=6.7.0 is an unpinned dependency, creating a minor supply-chain and reproducibility issue. The direct security impact is limited because it is a logging utility, but it still broadens the trusted dependency set.

Known Vulnerable Dependency: urllib3 — 10 advisory(ies): CVE-2025-66471 (urllib3 streaming API improperly handles highly compressed data); CVE-2024-37891 (urllib3's Proxy-Authorization request header isn't stripped during cross-origin ); CVE-2026-21441 (Decompression-bomb safeguards bypassed when following HTTP redirects (streaming ) +7 more

High

• Category: Supply Chain
• Confidence: 71% confidence
• Finding: Although the exact resolved urllib3 version is not pinned, the declared open-ended range means deployments may install versions affected by current or future advisories. Because urllib3 handles HTTP transport for remote image downloads, exploitation could lead to credential leakage, request smuggling/header issues, or denial of service in a core execution path.

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: 86% confidence
• Finding: Pillow is a high-risk dependency in this context because the skill is explicitly built to download and process original images, making untrusted image parsing a central function. The open-ended requirement means a deployment may resolve to vulnerable versions, and image-library flaws can lead to code execution, memory corruption, or severe resource exhaustion when handling crafted files.

Known Vulnerable Dependency: python-dotenv — 1 advisory(ies): CVE-2026-28684 (python-dotenv: Symlink following in set_key allows arbitrary file overwrite via )

Low

• Category: Supply Chain
• Confidence: 61% confidence
• Finding: Because python-dotenv is unpinned, environments could resolve to a vulnerable release affected by file-overwrite issues such as unsafe symlink handling. The impact is limited in this skill based on the dependency list alone, but it could matter if the code writes .env files or modifies environment configuration on shared systems.

Static analysis

No suspicious patterns detected.