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Security audit

AI-DLC

Security checks for vulnerabilities and agentic risk

Overview

The skill is a coherent AI-assisted development workflow, but it persistently records all user input verbatim and broadly reads/writes project files without enough privacy controls.

Review this skill carefully before installing. It may be useful for structured software delivery, but assume anything you type during the workflow can be copied into aidlc-docs/audit.md in plaintext. Do not paste secrets, tokens, customer data, or regulated information unless you first change the audit-log behavior, add redaction, or keep the audit file out of source control. Also expect it to scan the workspace and create or modify many aidlc-docs files, with application-code changes after approval.

Vulnerability Patterns
  • 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
  • Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
Findings (1)

T09 · Insecure Skill Coding Practices

Warning
Location
SKILL.md:81
Finding

Mandatory Verbatim Logging Can Persist Sensitive User Data

Content
View full analysis

Vulnerability Details

File Location: SKILL.md, lines 81-82
Vulnerability Type: Unredacted plaintext persistence of arbitrary user input
Risk Level: Medium

Vulnerable Code Snippet

markdown
### MANDATORY: 审计日志
**所有用户输入必须原样记录**到 `aidlc-docs/audit.md`(时间戳用 ISO 8601)。禁止总结或改写用户原话。

The associated persistence rule at SKILL.md, line 229 states:

markdown
- 审计日志:追加到 `audit.md`,禁止覆盖

This behavior conflicts with the security requirement at references/extensions/security/baseline/security-baseline.md, lines 73-82:

markdown
## Rule SECURITY-03: Application-Level Logging

**Rule**: Every deployed application component MUST include structured logging infrastructure:
- A logging framework MUST be configured
- Log output MUST be directed to a centralized log service
- Logs MUST include: timestamp, correlation/request ID, log level, and message
- Sensitive data (passwords, tokens, PII) MUST NOT appear in log output

**Verification**:
- Every service/function entry point includes a configured logger
- No ad-hoc logging statements used as the primary logging mechanism in production code
- Log configuration routes output to a centralized log service
- No secrets, tokens, or PII are logged

Technical Analysis

The Skill requires every user input to be copied verbatim into a persistent workspace file. The instruction is unconditional and provides no secret detection, redaction, user consent, retention limit, access restriction, or exclusion for passwords, API keys, tokens, personal data, and confidential source material.

Because the audit file is append-only, accidentally disclosed information remains present unless a user manually violates the stated no-overwrite rule or removes the file. The project may subsequently be committed to source control, copied into build artifacts, synchronized to backups, or shared with collaborators.

The bundled security baseline prohibits secrets, tokens, and personally identifiable information i ...[truncated 1708 chars]

Remediation
View remediation

Remediation Suggestions

  1. Replace verbatim transcript logging with structured summaries that record only workflow-relevant decisions and state changes.
  2. Apply secret and PII detection before every audit write. Redact passwords, API keys, bearer tokens, cookies, private keys, connection strings, and personal identifiers.
  3. Explicitly state that the security baseline overrides audit completeness whenever the two requirements conflict.
  4. Obtain informed user consent before retaining prompt content, and provide a supported opt-out mechanism.
  5. Define retention and deletion policies, including a command or workflow for securely removing audit data.
  6. Restrict file permissions so the audit file is readable only by the project owner where the platform permits it.
  7. Add aidlc-docs/audit.md to .gitignore by default, or store only a sanitized audit artifact suitable for source control.
  8. Validate audit content before persistence and reject or redact suspected credentials rather than writing them unchanged.
  9. Add tests covering secret-bearing prompts and verify that raw sensitive values never appear in the audit file.
  10. Update the rule to language such as:
markdown
Record a sanitized summary of each user interaction in
`aidlc-docs/audit.md`. Never store passwords, tokens, secrets, private
keys, PII, or other sensitive content. Obtain explicit consent before
retaining any raw user text.
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (24)

Missing User Warnings

High
Category
Not specified by scanner
Confidence
99% confidence
Finding

The skill requires all user input to be recorded verbatim to aidlc-docs/audit.md with timestamps, but provides no user-facing notice, consent flow, minimization rule, or secret-handling exception. This creates a strong risk of storing credentials, proprietary code, personal data, or regulated information in persistent plaintext artifacts inside the workspace.

Content

No source excerpt is available for this finding.

Ssd 3

High
Category
Not specified by scanner
Confidence
99% confidence
Finding

Verbatim retention of all user inputs is a direct data security risk because users often paste secrets, internal architecture, customer data, or incident details during development workflows. In this skill, the danger is amplified by broad triggers and mandatory file creation, making silent collection and persistence of sensitive natural-language content more likely.

Content

No source excerpt is available for this finding.

Missing User Warnings

High
Category
Not specified by scanner
Confidence
98% confidence
Finding

Requiring the agent to log the user's complete raw input to an audit file creates a high-risk privacy issue because prompts may contain credentials, internal URLs, customer data, or incident details. The lack of any privacy warning or consent mechanism makes accidental sensitive-data retention much more likely.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The trigger list includes very common phrases like 'start a project', 'new feature', and broad Chinese development phrases, which can cause the skill to activate in many ordinary software conversations. Over-broad activation increases the chance that users are funneled into this workflow unexpectedly, including its file-writing and logging behavior, without informed intent.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
87% confidence
Finding

The file is primarily written as mandatory Chinese workflow instructions and also prescribes fixed approval labels such as 'Request Changes' and 'Continue'. There is no explicit statement that the user may choose their preferred language or locale, which can violate language-choice policy when applied broadly.

Content

No source excerpt is available for this finding.

Autonomous Decision Making

Medium
Category
Excessive Agency
Confidence
75% confidence
Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · references/common/overconfidence-prevention.md (reported line 79)May include surrounding context.

md
## Quality Assurance

### Red Flags to Watch For
- Stages completing without asking any questions on complex projects
- Proceeding with vague or ambiguous user responses
- Skipping entire question categories without justification
- Making assumptions instead of asking for clarification

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The template explicitly instructs presenting options A/B and an answer field directly in the prompt, which implies in-chat multiple-choice interaction. Later guidance says to "DO NOT place the multiple-choice questions in-line in the chat session," creating a direct contradiction within the file's own instructions.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

The skill mandates automatic reading of a broad set of prior artifacts, including all code files and prior documents, without warning the user about the scope of access. This can unnecessarily ingest sensitive data into model context and expands exposure beyond what is needed for the immediate task, increasing privacy and data-minimization risks.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
88% confidence
Finding

The instruction to always log the continuity prompt to audit.md causes the agent to write to project files without an explicit user-facing notice or consent step. In an AI-driven development workflow, silent file writes can surprise users, pollute repositories, and persist sensitive context or prompts into version-controlled artifacts.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

The skill instructs the agent to create and save multiple files in the repository (plans, infrastructure-design, deployment-architecture, and possibly shared-infrastructure.md) without requiring an explicit user warning or confirmation before those write operations occur. In an agent setting, this can lead to unexpected repository modification, accidental overwrite of existing design artifacts, or creation of attacker-influenced files based on untrusted inputs from prior documents or user answers.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

The skill directs the agent to create and update many files in the workspace without an upfront warning that user data and repository contents will be modified. In practice, this can surprise users, overwrite work, or introduce generated artifacts into source control without informed consent.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The instruction to log the user's complete raw input to an audit file is not necessary for reverse-engineering a codebase and can capture secrets, personal data, or unrelated sensitive prompts. Persisting full user input increases exposure through local retention, later reuse, or accidental inclusion in commits and artifacts.

Content

No source excerpt is available for this finding.

Ssd 3

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

Natural-language instructions to retain complete user input create a data leakage path even without code execution: the model may persist sensitive text into files that can later be read, indexed, shared, or committed. In this skill's context, the danger is elevated because the workflow already writes many artifacts into the workspace, making unintended retention blend into normal outputs.

Content

No source excerpt is available for this finding.

Ssd 3

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The audit logging template requires persistent recording of the user's complete raw response, which is a classic data-minimization failure. In the context of an AI-driven software development workflow, users may paste stack traces, tokens, internal URLs, architecture details, or other confidential material into responses, so preserving verbatim text materially raises confidentiality and compliance risk.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The workflow explicitly instructs storing the user's COMPLETE RAW response in an audit log, which can capture secrets, credentials, personal data, or proprietary information the user may include while approving or modifying the plan. Because there is no minimization, redaction, consent notice, or retention control, this creates unnecessary sensitive-data retention and increases exposure through logs, backups, and downstream access.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

The skill directs the agent to scan the entire workspace, infer project metadata, and persist an absolute path and other state to disk without any notice, consent checkpoint, or scope limitation. This can expose sensitive source layout and filesystem information and creates unexpected side effects by writing files automatically, especially in shared, regulated, or privacy-sensitive environments.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill explicitly instructs automatic progression into later phases with no user approval, which can trigger additional analysis and file operations beyond the user's immediate expectation. This reduces user control and can compound privacy, cost, and unintended modification risks because subsequent steps may inspect more code or generate more artifacts automatically.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
80% confidence
Finding

This markdown file contains a natural-language requirement that forces a specific character set choice for all diagrams. Under the policy-violation rule, mandatory language/locale constraints should either provide user choice or clearly document a justified exception; here the compatibility rationale is stated, but the instruction is absolute and not framed as opt-in.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
83% confidence
Finding

The skill instructs the agent to create and update multiple project documentation, state, and audit files without requiring checks for existing content, backups, or user confirmation before overwriting. In an agent workflow, this can lead to silent clobbering of prior state, audit history, or manually maintained project documentation, reducing integrity and traceability.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
94% confidence
Finding

The skill instructs the agent to save a plan file into the workspace without explicitly warning the user that project documentation will be created or modified. While this is aligned with the skill’s stated purpose, silent file creation can still surprise users, overwrite expected artifacts, or cause unintended repository changes if the step is executed automatically.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
96% confidence
Finding

The skill directs generation of multiple functional design artifacts, potentially including frontend component documentation, but does not explicitly warn that several files will be written into the workspace. In a development workflow skill, broad unattended file generation increases the risk of unexpected changes, clutter, or overwriting prior design documents.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill requires updating audit and state-tracking files after approval, but does not clearly notify the user that workflow metadata files will be modified. Even though these are expected lifecycle artifacts, hidden changes to tracking files can affect project history, automation, or compliance expectations.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill directs the agent to create and save multiple files in the repository but does not require any explicit user-facing notice that repository contents will be modified. In an agentic workflow, this can lead to unexpected writes, accidental overwrites, or user approval bypass for generated artifacts, especially when run on a real project workspace.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
88% confidence
Finding

The skill instructs the agent to create multiple files and append approval data to an audit log, but it does not require any explicit user-facing disclosure that repository files will be modified. In an agentic development workflow, silent writes can surprise users, create unwanted persistence, and weaken informed consent around file-system changes, especially when logging user responses verbatim.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.