Security audit

客诉质量分析技能

Security checks for vulnerabilities and agentic risk

Overview

This skill is a local customer-complaint analysis helper with no evidence of hidden execution, network exfiltration, persistence, or privilege escalation, though users should handle complaint data carefully.

Install only if you are comfortable letting the agent read the complaint spreadsheets you provide. Redact customer names, contact details, IDs, and unnecessary free-text details before analysis when possible, and treat generated JSON, Markdown, and HTML reports as internal confidential documents.

A.I.G

Vulnerability Patterns

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
Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs

SkillSpector

Vulnerability Patterns

Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep

Findings (5)

Missing User Warnings

Medium

Category: Not specified by scanner
Confidence: 95% confidence
Finding: The skill is designed to ingest complaint ledgers, after-sales records, and customer exception lists, which commonly contain personal data, customer identifiers, contact details, and sensitive business information. The documentation focuses on analysis and report generation but omits any warning about data minimization, masking, access control, or the risk that generated HTML/Markdown reports may leak sensitive content if shared or stored insecurely.
Content: No source excerpt is available for this finding.

Missing User Warnings

Medium

Category: Not specified by scanner
Confidence: 95% confidence
Finding: The script includes raw data_preview rows and per-column sample_values from complaint datasets directly in its JSON output. Complaint files commonly contain personally identifiable information, customer names, contact details, incident descriptions, and other sensitive business data, so emitting these values without masking, minimization, or explicit consent creates a real data exposure risk.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

Category: Not specified by scanner
Confidence: 87% confidence
Finding: TRACE 说明中写有“支持中文交互”，结合全文均以中文名称和说明呈现，容易形成该技能默认或仅支持中文的语言约束，但文档没有向用户提供语言选择或说明这是特定中文业务场景的限定。根据语言/locale 策略检查，缺少用户选择或明确正当限制时应视为潜在自然语言政策问题。
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

Category: Not specified by scanner
Confidence: 82% confidence
Finding: The title and all user-facing instructions in this markdown file are presented exclusively in Chinese, with no indication that users may choose another language or that the skill is limited to a Chinese-speaking or region-specific context. Under the policy, forcing a specific language without user opt-in is a natural-language policy concern.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

Category: Not specified by scanner
Confidence: 84% confidence
Finding: The natural-language documentation strings and CLI descriptions are entirely in Chinese, with no indication that users may choose another language. Under the policy, forcing a specific language without opt-in can be a locale-policy violation unless clearly justified as region-specific.
Content: No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.