Security audit

MySQL慢查询分析器

Security checks for vulnerabilities and agentic risk

Overview

This is a local MySQL query-analysis helper that only parses user-provided SQL, EXPLAIN output, or slow-query logs and prints optimization advice.

Install only if a Chinese-language MySQL performance helper fits your workflow. Treat generated index and rewrite suggestions as heuristic advice to review with a DBA before applying to production schemas.

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

Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code

Findings (9)

Natural-Language Policy Violations

Medium

Confidence: 97% confidence
Finding: The skill description is written in Chinese and presents the tool's purpose in that language without indicating that other languages are supported or that the locale restriction is intentional. Under the policy, forcing a specific language without user opt-in is a natural-language policy concern.

Vague Triggers

Medium

Confidence: 88% confidence
Finding: In the trigger list, "查询优化" is a generic phrase meaning query optimization, which could match many ordinary requests rather than a clearly scoped invocation for this particular skill. The document does not provide exclusion conditions or negative examples to narrow when the skill should or should not activate.

Vague Triggers

Medium

Confidence: 84% confidence
Finding: "索引建议" can refer to a wide range of casual database conversations and is not narrowly tied to this skill's MySQL slow-query analysis workflow. Because the file lists triggers without boundaries or non-trigger examples, this broad phrase could cause unintended invocation.

Natural-Language Policy Violations

Medium

Confidence: 92% confidence
Finding: This Python file contains natural-language content in Chinese in the module description, and later functions emit warnings and suggestions only in Chinese. Under the stated policy, forcing a specific language without user opt-in is a natural-language policy violation unless the locale restriction is documented and justified.

Natural-Language Policy Violations

Medium

Confidence: 95% confidence
Finding: The code appends end-user-facing diagnostic messages exclusively in Chinese, and similar Chinese-only strings appear throughout the analyzer. Because the file provides no language selection mechanism or documented regional limitation, it violates the language/locale policy for all file types.

Natural-Language Policy Violations

Medium

Confidence: 97% confidence
Finding: This Python file contains natural-language descriptions and generated recommendation messages in Chinese, indicating the skill will present results in a specific language by default. The policy allows locale constraints only when the skill offers user choice or clearly documents a justified region-specific limitation, which is not present here.

Natural-Language Policy Violations

Medium

Confidence: 97% confidence
Finding: This code's natural-language interface, including the module docstring and all help/usage text, is presented only in Chinese. The policy requires flagging language or locale constraints when the skill forces a specific language without user opt-in, and no alternative language option or justification is provided here.

Natural-Language Policy Violations

Medium

Confidence: 93% confidence
Finding: This Python test file contains module docstrings and extensive test descriptions in Chinese, and there is no indication that users or contributors may opt into another language. Under the stated policy, forcing a specific language without user opt-in is a natural-language policy violation.

Natural-Language Policy Violations

Low

Confidence: 95% confidence
Finding: The module docstring and user-facing CLI/report strings are written in Chinese, making the skill effectively fixed to a specific language. The file does not provide any visible option for users to select another language or locale, which is a natural-language policy concern under the locale-choice rule.

Static analysis

No suspicious patterns detected.