Security audit

站酷每日推荐日报

Security checks for vulnerabilities and agentic risk

Overview

This skill is a straightforward ZCOOL design-work scraper and report generator, with some ordinary dependency and network-scope cautions.

Install it in a virtual environment as a normal user, consider pinning dependencies, and run it only in an environment where contacting ZCOOL and linked pages is acceptable. Be aware it creates or overwrites a same-day report file in the configured output directory.

A.I.G

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)

T08 · Insecure Dependencies

Warning

Location: SKILL.md:44
Finding: Unpinned Third-Party Python Dependencies
Content: View full analysis
Remediation: View remediation
beautifulsoup4== ``` 2. Generate and verify package hashes, then require them during installation: ```bash python3 -m pip install --require-hashes -r requirements.txt ``` 3. Commit the dependency file and its hashes to the project so installations are reproducible. 4. Periodically update pinned versions after vulnerability scanning and review rather than resolving unrestricted versions at installation time. 5. Recommend installation inside an isolated virtual environment without administrator or root privileges. 6. Consider using a trusted internal package mirror where package provenance and approved versions are enforced. ]]>

T09 · Insecure Skill Coding Practices

Warning

Location: scripts/zcool_daily.py:173
Finding: Unrestricted Fetching of Page-Derived Absolute URLs
Content: View full analysis
Remediation: View remediation

SkillSpector

Vulnerability Patterns

Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
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
Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access

Findings (9)

Undeclared Tool Scope

Medium

Category: MCP Least Privilege
Confidence: 95% confidence
Finding: The skill documentation indicates network access and local file creation, but it does not declare any explicit tool scope or permissions. This creates a least-privilege and transparency problem: an agent or user may invoke the skill without clear constraints on external access or filesystem writes, increasing the chance of unintended data access or side effects.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium

Category: Not specified by scanner
Confidence: 88% confidence
Finding: The natural-language content of the skill, including its description and usage guidance, is entirely in Chinese. Under the stated policy, forcing a specific language without user opt-in or documented justification is a locale-policy concern.
Content: No source excerpt is available for this finding.

Missing User Warnings

Low

Category: Not specified by scanner
Confidence: 88% confidence
Finding: The markdown states that the skill writes output to a local file, but it does not clearly warn users before execution that running the skill will create files on disk. This is a transparency and consent issue: unexpected filesystem side effects can surprise users, clutter environments, or overwrite expected artifacts if naming or paths are misunderstood.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

Category: Not specified by scanner
Confidence: 88% confidence
Finding: This markdown file presents the skill output, instructions, and labels exclusively in Chinese. Under the stated policy, forcing a specific language without offering the user a choice or documenting a justified locale constraint can be a natural-language policy violation.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

Category: Not specified by scanner
Confidence: 91% confidence
Finding: The natural-language description and user-facing CLI/help text are presented exclusively in Chinese, which can amount to a language policy constraint without user opt-in. There is no indication that the skill is intentionally limited to Chinese-speaking users or region-specific usage.
Content: No source excerpt is available for this finding.

Intent-Code Divergence

Low

Category: Not specified by scanner
Confidence: 99% confidence
Finding: The top-level documentation says the script is used with 'python3 zcool_daily.py --mode list' to get a works list. However, the actual argument parser only defines '--output-dir' and has no '--mode' option, so the documented invocation contradicts the real behavior.
Content: No source excerpt is available for this finding.

Missing User Warnings

Low

Category: Not specified by scanner
Confidence: 81% confidence
Finding: This code fetches content from external URLs using HTTP requests, which is a network operation covered by the warning requirement for code files. While the script's purpose implies retrieving ZCOOL content, the module docstring and argument help do not clearly disclose that it will contact the live website; only a runtime progress message appears later, after execution has started.
Content: No source excerpt is available for this finding.

Description-Behavior Mismatch

Low

Category: Not specified by scanner
Confidence: 88% confidence
Finding: The manifest description frames the skill as an information retrieval capability for ZCOOL hot works. In addition to fetching and formatting that data, the code creates an output directory and persists the results as a dated text file on disk, which is extra behavior not conveyed by the manifest description.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

Category: Not specified by scanner
Confidence: 92% confidence
Finding: This plain-text skill content is entirely presented in Chinese and does not indicate any language choice, opt-in, or region-specific justification. Under the policy rule for language/locale constraints, this is a natural-language policy issue because the skill appears to impose a specific language on users without documented flexibility.
Content: No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.