Audit Report: xiaohongshu-skills — 🟠 D (69/100)
Audited by TAR Engine · 2026-09-09 · Report format v0.2
Reading note: this edition uses gpt-4o-mini as the victim model and the same model as the adversarial-fuzz judge. Findings reflect missing defenses in the SKILL.md itself — not a verdict on any specific victim model. The remediation belongs in SKILL.md, not in the model.
Source: https://github.com/autoclaw-cc/xiaohongshu-skills/blob/main/SKILL.md
Verdict: High risk — 3 high-severity issues need author attention before deploying to a shared environment.
What this skill does
Auditor's read (LLM-generated): The xiaohongshu-skills enables users to automate interactions with the Xiaohongshu platform, including logging in, posting content, searching for notes, and engaging in social interactions like commenting and liking. It strictly executes commands through a designated Python script (cli.py), ensuring all operations are structured and confirmed by the user, with outputs provided in JSON format. The skill encompasses various sub-skills for authentication, content publishing, exploration, interaction, and complex operational workflows.
Author description: |
Observed: xiaohongshu-skills is 6 top-level sections (🔒 技能边界（强制）, 输入判断, 全局约束, 子技能概览, 快速开始, …); ~125 lines of instructions, delegates to packaged scripts, concise body.
Frontmatter facts:
- Body size: 125 lines / 2645 chars
Score breakdown by category
Each category gets its own sub-score. A category with no rule hits gets 100; a category with a single critical finding drops to 80.
| Category | Rules evaluated | Findings | Max severity | Sub-score |
|---|---|---|---|---|
| Prompt injection / scope override | 5 | 1 | 🟠 high | 90/100 |
| Shell safety | 4 | 1 | 🟠 high | 90/100 |
| Sensitive file access | 1 | 0 | ⚪ none | 100/100 |
| Data exfiltration | 3 | 0 | ⚪ none | 100/100 |
| Credential exposure | 1 | 0 | ⚪ none | 100/100 |
| Malicious payload signatures | 3 | 1 | 🟠 high | 90/100 |
| Supply chain (deps + CVE) | 0 | 0 | ⚪ none | 100/100 |
| quality | 2 | 1 | 🔵 info | 99/100 |
Historical baseline (same-skill comparison)
- Prior audits on record: 2 (first 2026-06-11T21:08:46.695597Z, most recent prior 2026-06-11T21:11:19.311710Z)
- Score statistics: mean 60.0 ± 28.3 (range 40–80) (normal band: 31.7 – 88.3)
- This audit vs last: -11 (📉 regressed)
- Top recurring findings across history:
SEM-002— hit in 2 of 2 prior audits (100.0%)SEM-007— hit in 2 of 2 prior audits (100.0%)SEM-006— hit in 1 of 2 prior audits (50.0%)SEM-008— hit in 1 of 2 prior audits (50.0%)SEM-003— hit in 1 of 2 prior audits (50.0%)
Baseline assumes the skill's name + description haven't changed. A rename or rewrite starts a fresh baseline.
Findings
4 rules matched. Each finding below cites the matched line and a remediation hint.
1. 🟠 SEM-008 — external_payload_blind_trust (HIGH)
- Category: Malicious payload signatures
- Why this matched: The skill assumes that the Chrome launcher script will always function correctly without validating the environment or the state of the browser, which could lead to security issues if the script is compromised.
- Rule intent: Trusts external content (downloaded file, remote prompt template, third-party output) without validation
- Matches in document: 1
Evidence (1 of 1 match):
Line 106:
# 1. 启动 Chrome
Suggested fix: Add checks to ensure that the Chrome launcher script executes successfully and that the browser is in a secure state before proceeding with further actions.
2. 🟠 SEM-002 — ambiguous_instruction (HIGH)
- Category: Prompt injection / scope override
- Why this matched: The instruction to confirm before publishing or commenting is vague and could lead to unintended actions if a user input is crafted to bypass this confirmation.
- Rule intent: Ambiguous instruction that could be exploited as a prompt injection primitive
- Matches in document: 1
Evidence (1 of 1 match):
Line 48:
发布和评论操作必须经过用户确认后才能执行。
Suggested fix: Clarify the confirmation process by specifying how the user should confirm their actions, possibly by requiring a specific command or keyword.
3. 🟠 SEM-007 — irreversible_action_no_confirmation (HIGH)
- Category: Shell safety
- Why this matched: The command to delete cookies is irreversible and does not require explicit user confirmation, which could lead to accidental data loss.
- Rule intent: Skill instructs the LLM to take an irreversible action without explicit user confirmation
- Matches in document: 1
Evidence (1 of 1 match):
Line 65:
| `cli.py delete-cookies` | 清除 cookies（退出/切换账号） |
Suggested fix: Implement a confirmation step before executing the delete-cookies command, such as prompting the user to type 'yes' to confirm the action.
4. 🔵 QL-001 — shell_block_no_error_handling (INFO)
- Category: quality
- Why this matched: Shell block missing
set -e/|| exit— silent failures will go unreported - Rule intent: Shell code blocks without
set -eor explicit error handling - Matches in document: 1
Evidence (1 of 1 match):
Line 105:
104:
>> 105: ```bash
>> 106: # 1. 启动 Chrome
>> 107: python scripts/chrome_launcher.py
>> 108:
>> 109: # 2. 检查登录状态
>> 110: python scripts/cli.py check-login
>> 111:
>> 112: # 3. 登录（如需要）
>> 113: python scripts/cli.py login
>> 114:
>> 115: # 4. 搜索笔记
>> 116: python scripts/cli.py search-feeds --keyword "关键词"
>> 117:
>> 118: # 5. 查看笔记详情
>> 119: python scripts/cli.py get-feed-detail \
>> 120: --feed-id FEED_ID --xsec-token XSEC_TOKEN
>> 121:
>> 122: # 6. 发布图文
>> 123: python scripts/cli.py publish \
>> 124: --title-file title.txt \
>> 125: --content-file content.txt \
>> 126: --images "/abs/path/pic1.jpg"
>> 127:
>> 128: # 7. 发表评论
>> 129: python scripts/cli.py post-comment \
>> 130: --feed-id FEED_ID \
>> 131: --xsec-token XSEC_TOKEN \
>> 132: --content "评论内容"
>> 133:
>> 134: # 8. 点赞
>> 135: python scripts/cli.py like-feed \
>> 136: --feed-id FEED_ID --xsec-token XSEC_TOKEN
>> 137: ```
138:
Suggested fix: Add set -euo pipefail at the top of bash blocks, or chain critical commands with || exit 1. Skills that fail silently mid-script are nearly impossible to debug downstream.
Scope of this edition
The audit covers static rule matching, semantic-layer LLM analysis, and adversarial prompt fuzzing. Three classes of risk live beyond this edition's scope. We name them explicitly:
- Runtime behavior. Verifying what a skill actually does at runtime requires sandboxed execution. That layer ships in a future edition; today's report reflects what the skill states it will do, plus the LLM's read of how it would behave.
- Cross-skill composition. When this skill is chained with others through a planner, the emergent state flow between skills is its own analysis surface. Out of scope for single-skill reports.
- External payloads. A skill that fetches and runs a remote script is flagged at the fetch step. The remote payload itself is audited as a follow-up once the sandbox layer is online.
Methodology
How the score was computed:
- Document text is scanned against a static rule set of 32 signature patterns. Each rule carries a permanent
rule_id(e.g.PI-001), a category, a severity, and a remediation template. - Each rule hit deducts from a 100-point base: critical -20, high -10, warning -5, info -1.
- The letter grade is gated by max severity AND total score: any critical → F; any high → at most D; any warning → at most C; otherwise A/B by score band.
- Per-category sub-scores apply the same deduction formula to that category's findings only — so you can see WHICH risk surface drove the loss.
Rule matches are augmented by an LLM-based semantic pass when an LLM endpoint is configured. The semantic pass uses rule IDs SEM-001 … SEM-008.
When an LLM endpoint is configured the skill is also probed with a 15-attack adversarial corpus (5 classes × 3 prompts), each judged by a separate LLM call. Failed classes surface as rule IDs AR-001 … AR-005.
Engine + rule set provenance:
- Engine version:
0.2.0 - Rule set version:
1.1.0 - Commit:
unknown - Domain config:
general - Audited at:
2026-09-09T20:25:39.049671Z - Rules applied: 36 static rules (full registry below)
Full rule registry applied to this audit
| Rule ID | Name | Category | Severity | |---|---|---|:---:| | `FA-001` | sensitive_file_access | file_access | warning | | `SS-001` | destructive_bash | shell_safety | high | | `SS-002` | force_flag_abuse | shell_safety | high | | `DE-001` | external_data_exfil | data_exfil | high | | `CE-001` | credential_in_content | credential_exposure | high | | `SS-003` | pipe_to_shell | shell_safety | critical | | `SS-004` | sudo_usage | shell_safety | warning | | `PI-001` | prompt_injection_bypass | prompt_injection | critical | | `PI-002` | hidden_instruction | prompt_injection | critical | | `PI-003` | excessive_permission_claim | prompt_injection | high | | `PI-004` | disable_safety_instruction | prompt_injection | high | | `PI-005` | impersonation_instruction | prompt_injection | high | | `MP-001` | encoded_payload | malicious_payload | warning | | `DE-002` | network_exfil_pattern | data_exfil | high | | `MP-002` | crypto_miner_pattern | malicious_payload | critical | | `MP-003` | reverse_shell_pattern | malicious_payload | critical | | `DE-003` | data_collection_broad | data_exfil | warning | | `QL-001` | shell_block_no_error_handling | quality | info | | `QL-002` | unpinned_install_command | quality | info | | `SEM-001` | semantic_evasion | prompt_injection | high | | `SEM-002` | ambiguous_instruction | prompt_injection | warning | | `SEM-003` | capability_overreach | prompt_injection | warning | | `SEM-004` | prompt_injection_subtle | prompt_injection | high | | `SEM-005` | unauthorized_data_flow | data_exfil | high | | `SEM-006` | credential_handling_unsafe | credential_exposure | high | | `SEM-007` | irreversible_action_no_confirmation | shell_safety | high | | `SEM-008` | external_payload_blind_trust | malicious_payload | high | | `AR-001` | instruction_override_failure | prompt_injection | high | | `AR-002` | role_jailbreak_failure | prompt_injection | high | | `AR-003` | hidden_payload_failure | malicious_payload | high | | `AR-004` | authority_spoof_failure | prompt_injection | high | | `AR-005` | reflective_injection_failure | prompt_injection | high | | `SUP-001` | typosquat_risk | supply_chain | high | | `SUP-002` | known_vulnerability | supply_chain | high | | `SUP-003` | unpinned_dependency | supply_chain | warning | | `SUP-004` | deprecated_or_yanked | supply_chain | warning |Known limitations of this report
- False positives are possible. A SKILL.md documenting a dangerous pattern (e.g. an audit skill explaining
curl | sh) will match the rule even though the skill's intent is to detect, not execute. Read the matched lines before reacting. - False negatives are guaranteed in narrow ways. Patterns obfuscated by string concatenation, environment variable indirection, or non-English equivalents will slip past regex.
- Baseline sample size. Same-skill trend analysis (§ Historical baseline) gets meaningful with n≥3 prior audits. With fewer priors the stddev band is widened to avoid false out-of-band signals.
About TAR Engine
TAR Engine is an OSS "wish machine" with built-in audit. Speak a goal; the engine plans, runs and audits skills inside its own container. BYOK. — github.com/qingxuantang/tar-engine