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Security audit

WotoHub Automation

Security checks for vulnerabilities and agentic risk

Overview

The skill is broadly consistent with WotoHub outreach automation, but it exposes high-impact local command execution, credential handling, URL fetching, scheduling, and email/inbox actions with unsafe scoping.

Review before installing. Use this only in an isolated environment, keep TLS verification enabled, avoid passing untrusted product URLs, request config, executor fields, or briefPath values, and do not rely on CLI token entry unless you are comfortable with plaintext local persistence. Constrain host bridge executors to fixed trusted commands and remember that scheduled cycles may send real outreach emails and low-risk replies automatically.

Vulnerability Patterns
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs
  • 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
Findings (6)

T09 · Insecure Skill Coding Practices

Error
Location
wotohub_skill.py:146
Finding

Request-Controlled Arbitrary Command Execution with Inherited Credentials

Content
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tuple[dict, Optional[dict]]: request = _extract_host_url_analysis_request(orchestrated) if not request: return req, None config = req.get("config", {}) or {} executor = None if hasattr(orchestrator, "_get_host_analysis_executor"): executor = orchestrator._get_host_analysis_executor(config=config) if not executor: return req, { "status": "no_executor_configured", "request": request, } payload = orchestrator._run_host_analysis_executor(request=request, executor_spec=executor) ``` ```python # scripts/orchestrator.py:509-570 def _get_host_analysis_executor(self, *, config: dict[str, Any]) -> Any: for key in ("hostAnalysisExecutor", "host_analysis_executor", "hostSemanticExecutor", "host_semantic_executor"): if config.get(key): return config.get(key) env_default = os.environ.get("WOTOHUB_HOST_ANALYSIS_EXECUTOR") or os.environ.get("HOST_ANALYSIS_EXECUTOR") if env_default: return env_default return None def _run_host_analysis_executor(self, *, request: dict[str, Any], executor_spec: Any) -> Any: skill_root = Path(__file__).resolve().parent.parent with tempfile.TemporaryDirectory(prefix="wotohub-host-analysis-") as tmp: tmp_dir = Path(tmp) input_path = tmp_dir / "host-analysis-request.json" output_path = tmp_dir / "host-analysis-output.json" input_path.write_text(json.dumps(request, ensure_ascii=False, indent=2), encoding="utf-8") timeout_seconds = 180 cwd = str(skill_root) env_o ...[truncated 4131 chars]
Remediation
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T09 · Insecure Skill Coding Practices

Error
Location
scripts/url_fetcher.py:60
Finding

Server-Side Request Forgery Through Unrestricted Product URL Fetching

Content
View full analysis
bool: v = value.strip() return v.startswith('http://') or v.startswith('https://') ``` ```python # scripts/product_resolve.py:399-400 fetched = fetch_url_text(raw, timeout=timeout) html = fetched['html'] ``` ```python # scripts/url_fetcher.py:60-75 def fetch_url_text(url: str, timeout: int = 12, retries: int = 2, verify_tls: Optional[bool]= None) -> dict: session = _create_session(retries) last_exc = None if verify_tls is None: verify_tls = os.environ.get('WOTOHUB_INSECURE_FETCH', '').strip().lower() not in {'1', 'true', 'yes'} for attempt in range(retries + 1): profile = _HEADER_PROFILES[min(attempt, len(_HEADER_PROFILES) - 1)] headers = { 'User-Agent': profile['User-Agent'], 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8', 'Accept-Language': profile['Accept-Language'], 'Accept-Encoding': 'gzip, deflate', 'Cache-Control': 'no-cache', 'Pragma': 'no-cache', 'Connection': 'keep-alive', 'Upgrade-Insecure-Requests': '1', 'Referer': url, } try: resp = session.get(url, headers=headers, timeout=timeout, verify=verify_tls, allow_redirects=True) ``` ### Technical Analysis The URL validation only checks whether the input starts with `http://` or `https://`. It does not: - Resolve and inspect the destination IP address. - Reject loopback, private, link-local, reserved, multicast, or unspecified addresses. - Restrict destination ports. - Restrict hosts to supported public product platforms. - Revalidate destinations after HTTP redirects. - Pr ...[truncated 1541 chars]
Remediation
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T09 · Insecure Skill Coding Practices

Error
Location
scripts/common.py:58
Finding

Plaintext API-Key Persistence Without Explicit Restrictive Permissions

Content
View full analysis
str: token = (token or "").strip() if not token: raise ValueError("api-key is empty") ensure_state_dir() settings = _load_settings() settings["apiKey"] = token _SETTINGS_PATH.write_text(json.dumps(settings, ensure_ascii=False, indent=2)) return token ``` ```python # scripts/common.py:104-108 def get_token(cli_token: Optional[str]= None, required: bool = True, feature: Optional[str] = None) -> Optional[str]: token = cli_token or os.environ.get("WOTOHUB_API_KEY") or get_saved_token() or DEFAULT_API_KEY if cli_token: try: save_token(cli_token) ``` ### Technical Analysis When a token is supplied through the CLI, `get_token()` attempts to persist it automatically. `save_token()` writes the credential in plaintext JSON under the configured state directory. The implementation does not explicitly create the state directory with mode `0700` or the settings file with mode `0600`. Effective permissions therefore depend on the process umask and any pre-existing directory or file. It also does not verify ownership, reject symlinks, or perform an atomic secure write. Automatic persistence is particularly risky because a caller supplying a one-time CLI token may not expect it to be stored across sessions. ### Attack Path 1. A user or automation invokes an authenticated operation and supplies a token through the CLI. 2. `get_token()` calls `save_token()` automatically. 3. The token is written to `settings.json` as plaintext. 4. Another local p ...[truncated 617 chars]
Remediation
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T09 · Insecure Skill Coding Practices

Error
Location
scripts/common.py:17
Finding

TLS Certificate Verification Can Be Disabled for Authenticated API Requests

Content
View full analysis
dict: session = _create_session(retries) last_exc = None if verify_tls is None: verify_tls = os.environ.get('WOTOHUB_INSECURE_FETCH', '').strip().lower() not in {'1', 'true', 'yes'} ... resp = session.get(url, headers=headers, timeout=timeout, verify=verify_tls, allow_redirects=True) ``` ### Technical Analysis Setting `WOTOHUB_VERIFY_TLS` to `0`, `false`, or `no` disables both certificate validation and hostname verification for WotoHub API requests. Those requests may contain the WotoHub API key i ...[truncated 1324 chars]
Remediation
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T05 · Unauthorized Access and Privilege Escalation

Warning
Location
wotohub_skill.py:214
Finding

Unrestricted Local JSON File Read Through briefPath

Content
View full analysis
Optional[dict]: input_data = req.get("input", {}) or {} brief = input_data.get("brief") if isinstance(brief, dict): return brief brief_path = input_data.get("briefPath") if not brief_path: return None try: loaded = json.loads(Path(brief_path).read_text(encoding="utf-8")) except Exception: return None return loaded if isinstance(loaded, dict) else None ``` ### Technical Analysis The public stdin request controls `input.briefPath`. The implementation reads that path directly without: - Restricting it to a trusted campaign directory. - Rejecting absolute paths or traversal components. - Resolving and checking path containment. - Rejecting symlinks. - Checking file ownership or permissions. Only valid JSON objects are returned, which limits the file types that can be consumed, but any readable local JSON object can still be interpreted as a campaign brief. A loaded file may also contain fields that affect subsequent orchestration, including unsafe executor specifications where accepted by downstream code. ### Attack Path 1. An attacker submits a campaign-cycle request with `input.briefPath` set to an absolute path or traversal path. 2. The Skill process opens the path using its own filesystem privileges. 3. If the target contains a valid JSON object, it is loaded as the campaign brief. 4. Values from that file enter campaign preparation and execution. 5. Depending on downstream output and behavior, the attacker may obtain file-derived data or trigger operations configured by the loaded object. ### Impact Assessment The issue crosses the intended request-to-filesystem boundary and may expose readable JSON configuration or state to campaign processing. It can also am ...[truncated 242 chars]
Remediation
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T08 · Insecure Dependencies

Note
Location
requirements.txt:1
Finding

Non-Reproducible Third-Party Dependency Version Selection

Content
View full analysis
=2.31,<3 ``` ### Technical Analysis The dependency specification permits installation of any future `requests` release below version 3. As a result, two installations performed at different times may receive different code without a corresponding project change or review. No typosquatted package, untrusted package source, or known malicious version was identified in the reviewed file. The issue is limited to build reproducibility and future supply-chain exposure. ### Attack Path 1. The project is installed using `pip install -r requirements.txt`. 2. The package index selects the newest version satisfying `>=2.31,<3`. 3. A newly released or compromised version can be installed without modification of the project repository. 4. That dependency executes within the Skill process and receives the process's network and filesystem privileges. ### Impact Assessment A vulnerable or compromised future dependency release could affect all network-enabled features using `requests`, including product URL retrieval and semantic API communication. The current specification also makes security testing and deployment reproduction less reliable. ]]>
Remediation
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Vulnerability Patterns
  • Taint TrackingDirect Taint Flow, Variable-Mediated Taint Flow, Credential Exfiltration Chain
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (213)

Tainted flow: 'req' from os.environ.get (line 140, credential/environment) → urllib.request.urlopen (network output)

Critical
Category
Data Flow
Confidence
90% confidence
Finding

Credentials or environment variables flow to a network sink. This is a high-confidence indicator of credential exfiltration.

Content

Scanner excerpt · scripts/common.py (reported line 142)May include surrounding context.

python
data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
    req = urllib.request.Request(url, data=data, headers=headers, method=method)
    try:
        with urllib.request.urlopen(req, timeout=timeout, context=_SSL_CONTEXT) as resp:
            raw = resp.read().decode("utf-8")
    except urllib.error.HTTPError as e:
        raw = e.read().decode("utf-8", errors="replace")

Tp4

High
Category
MCP Tool Poisoning
Confidence
96% confidence
Finding

The description presents a full-featured influencer outreach automation skill. However, this code chunk is only a scheduler/launcher wrapper around another script (wotohub_skill.py). Its concrete behavior is limited to reading arguments and JSON config, constructing a request payload, preparing environment variables, and invoking the downstream entrypoint. While this may support the broader system, the supplied code itself does not perform the declared end-to-end campaign functions. Therefore the code chunk's actual behavior is materially narrower and different in primary purpose from the declared description.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
99% confidence
Finding

There is a clear description-behavior mismatch. The declared purpose describes a broad campaign automation skill for influencer outreach workflows, but the actual code chunk contains only a generic retry wrapper with exponential backoff. While such a helper could support API calls inside a larger system, this specific chunk does not implement any of the declared user-facing capabilities, triggers, or resource interactions. This is not merely a supporting detail mismatch in isolation; the supplied code’s observable behavior is materially different from the declared primary purpose.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

Most of the code aligns with the declared outreach automation workflow: it supports product analysis, search, recommendation formatting, email draft handling, email sending, and reply monitoring. However, the code also includes a substantial additional capability not reflected in the description: campaign creation and scheduling. The _campaign_create path builds campaign plans/briefs, saves registry entries, and can invoke openclaw cron add through subprocess.run using environment-provided gateway credentials to create scheduled jobs. This is more than an implementation detail; it is an undeclared capability involving persistence and external scheduling/orchestration resources. Therefore the description does not fully and accurately represent the actual behavior.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
94% confidence
Finding

The supplied code chunk is a narrow utility module for schema normalization and validation of campaign briefs. It does not implement the declared end-to-end outreach workflow, such as searching creators, ranking recommendations, generating or sending emails, reviewing inbox messages, or assisting with replies. While brief normalization is a supporting component for such a system, the declared description represents a much broader operational skill than what this code actually does. Therefore the description does not accurately represent this specific code chunk's behavior.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

The supplied code chunk is narrowly focused on inbox data retrieval and normalization for conversation-analysis input. It calls inbox listing/detail/dialogue functions, filters unread/unreplied messages, and writes structured JSON for later analysis. It does not implement creator discovery, ranking, product understanding, email generation, sending, or full outreach automation. While inbox review/conversation analysis is related to part of the declared description, the declared purpose substantially overstates the actual behavior of this code chunk, making the description materially inaccurate for the provided code.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
98% confidence
Finding

The declared description presents a large, end-to-end influencer outreach automation capability spanning many campaign stages. The supplied code chunk is much narrower: it reads candidate conversation data from a file, retrieves full message threads for selected chat IDs from an inbox service, normalizes fields, and emits a JSON payload instructing downstream conversation analysis. While this could be a supporting component of the declared inbox review or guarded reply assist workflow, its actual purpose is specifically conversation-thread context assembly, not the broad automation described. Because the description materially overstates the functionality represented by this code chunk and omits the concrete inbox-thread retrieval behavior, this is a mismatch.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
98% confidence
Finding

The supplied code chunk only implements category classification logic. It reads local reference JSON files, expands semantic labels/aliases, scores token overlap against a category tree, and outputs selected category IDs plus confidence metadata. It does not contain functionality for influencer discovery, campaign orchestration, email generation/sending, inbox processing, or reply assistance as claimed in the description. This is a material description-behavior mismatch because the actual code’s primary purpose is category inference, not end-to-end influencer outreach automation.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

The declared description presents a broad, end-to-end influencer outreach automation capability spanning search, recommendations, email drafting/sending, inbox review, and reply assistance. However, the supplied code chunk only implements the creator-search and recommendation portion: payload construction, API querying, fallback strategies, creator extraction, heuristic scoring, ranking, and display formatting. It does fit part of the declared purpose—creator search and recommendation ranking for WotoHub—but it does not implement the outreach, inbox, or reply-assist stages claimed in the description. Because the description materially overstates the behavior of this code chunk, this is a mismatch.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
96% confidence
Finding

The declared description presents a broad end-to-end influencer outreach automation system. The supplied code does not implement those campaign functions; instead, it provides a state-management utility for storing and updating email/chat processing status in conversation_state.json. While this could be a supporting component within such a larger system, this chunk’s actual primary purpose is workflow state persistence and skip/cooldown checks, not outreach automation itself. Because the code chunk materially differs from the declared end-to-end functional description and exposes a narrower, different capability set, this is a mismatch.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
98% confidence
Finding

The code chunk is a focused validation/auditing module, not an end-to-end outreach automation system. It inspects existing email content, normalizes it via a send-guard helper, compares mentioned platform/country/language/category cues against creator profile metadata, and returns errors/warnings/checks. It also builds a creator profile map and audits collections of emails. While this could support guarded outreach workflows, the declared description substantially overstates the implemented behavior by claiming creator discovery, recommendation ranking, drafting, sending, inbox review, and reply assistance—none of which appear in this code. Therefore the description does not accurately represent this specific code chunk.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
97% confidence
Finding

The declared description presents a campaign automation skill centered on influencer outreach workflows. The supplied code does not implement any of those domain functions or interact with campaign, creator, email, inbox, or ecommerce resources. Instead, it provides infrastructure-level error handling and retry logic. While such utilities could support a larger outreach system, this chunk’s actual purpose is materially different from the declared end-to-end outreach functionality, so this code chunk does not accurately represent the described skill behavior.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
96% confidence
Finding

The description presents a comprehensive outreach automation skill spanning discovery, ranking, drafting, sending, and inbox/reply operations. However, the actual code chunk is narrowly focused on generating outreach emails from existing input data. It reads JSON input, extracts influencer items, normalizes product names/selling points, optionally consumes host-model drafts, and otherwise generates fallback email drafts in several languages. It outputs structured draft data but does not send emails, search creators, rank recommendations, review inbox messages, or assist with replies. Because the declared purpose materially overstates the code chunk’s primary behavior and includes several undeclared-in-code capabilities, this is a mismatch.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
98% confidence
Finding

The declared description promises a broad, end-to-end influencer outreach system covering discovery, ranking, communication drafting, sending, and inbox/reply handling. The supplied code only parses an input JSON file, constructs a mostly hard-coded/example host analysis object from partial signals, derives a compact product summary, and prints or writes the result. There is no logic for searching creators, ranking recommendations, drafting emails, sending messages, reviewing inbox content, or assisting with replies. The code’s actual purpose is much narrower: an example host analysis executor for product/campaign summarization. That is a material mismatch in primary purpose and capabilities.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
98% confidence
Finding

The declared description presents a broad end-to-end influencer outreach automation capability. The supplied code chunk is much narrower: it is an example executor that accepts selected creators and a brief, then produces templated email drafts and metadata. Its primary purpose is draft generation, not full campaign orchestration. There is no evidence of creator discovery, recommendation scoring, message sending, inbox access/review, or reply assistance. This is a material description-to-behavior mismatch rather than a minor implementation subset.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

The declared description presents a comprehensive end-to-end influencer outreach system. The supplied code chunk is much narrower: it is an example bridge executor that reads input JSON, creates mock draft outreach messages for preselected creators, packages output, and optionally writes it to disk. Its primary purpose is example/mock draft generation and writeback formatting for integration with a larger campaign runner, not the full outreach lifecycle. There is no evidence in this chunk of product understanding, creator discovery, recommendation ranking, outbound sending, inbox inspection, or reply assistance. This is a material description-versus-behavior mismatch.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
94% confidence
Finding

The declared description presents a broad, end-to-end influencer outreach automation system spanning discovery, ranking, outreach drafting, sending, inbox review, and guarded reply assistance. The supplied code chunk does only a small subset: it processes reply-analysis input and emits a structured reply analysis payload with many hardcoded defaults. It does not search creators, rank recommendations, draft outreach sequences, send messages, or perform product understanding. The code is consistent with a reply-assist/example executor component, but not with the full declared primary purpose. Therefore the description materially overstates what this code chunk actually does.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

The declared description presents a broad campaign automation system for influencer outreach across multiple workflow stages. The supplied code chunk does not implement those capabilities. Instead, it only provides helper functions for formatting and sanitizing email body content. While email drafting support could be a small supporting part of outreach, this module alone is far narrower than the declared primary purpose, so the description does not accurately represent what this code chunk actually does.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
97% confidence
Finding

The declared description presents a broad, end-to-end influencer outreach automation system with model-first planning capabilities, including product understanding, creator search, recommendation ranking, drafting, batch send, inbox review, and guarded reply assistance. The supplied code only implements inbox and email operations against /email/* endpoints: list inbox, get email details, get dialogue, send/schedule emails, and reply. Inbox review and batch send are represented, but the majority of the declared workflow is absent from this code chunk. There is no evidence here of creator discovery, campaign planning, product analysis, ranking/recommendation, or any guarded/AI-assisted reply generation. This is therefore a description-behavior mismatch: the code is a narrower mailbox operations component, not the described end-to-end outreach automation capability.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
82% confidence
Finding

The declared description presents a full end-to-end automation skill covering planning plus operational execution steps such as creator recommendation ranking, outreach sending, inbox review, and reply assistance. The supplied code chunk does not implement those actions. Instead, it is a preprocessing/understanding layer that parses user input into normalized context and inferred metadata. It even states it must not directly execute WotoHub API actions. While the code supports the 'product understanding' portion of the description and can infer intended tasks like search, email generation, campaign creation, and reply monitoring, it does not carry out those tasks. Therefore the description materially overstates the implemented behavior of this code chunk.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
99% confidence
Finding

The declared description promises a broad, end-to-end influencer outreach automation workflow. The supplied code only implements a narrow heuristic text-parsing backend that extracts semantic campaign attributes from input text and returns structured JSON. It has no integrations, no creator discovery logic, no ranking engine, no messaging/email generation, no send capability, and no inbox/reply processing. While 'product understanding' is partially related, the primary behavior is materially narrower than the declared purpose, so this is a clear mismatch.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
99% confidence
Finding

The supplied code chunk is narrowly focused on product-page resolution and metadata extraction from HTML/text using regex and JSON-LD parsing. It does not implement influencer discovery, ranking, email drafting, message sending, inbox processing, or reply assistance. While product understanding could be a supporting part of the declared system, this chunk’s actual behavior is only a platform-specific product parser, which is materially narrower and different from the declared end-to-end outreach automation purpose.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
98% confidence
Finding

The supplied code chunk does not implement any outreach, creator discovery, ranking, messaging, inbox handling, or campaign automation behavior. Its actual function is a local data-loading utility that reads category/region/language reference files and prepares in-memory dictionaries and alias maps. While such data support could be a small internal helper within a larger outreach system, this specific chunk's primary purpose is materially different from the declared end-to-end outreach automation description. Therefore, the description does not accurately represent what this code chunk actually does.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
94% confidence
Finding

The description presents a broad end-to-end WotoHub influencer outreach system, but the actual code chunk is narrowly scoped to conservative local product-analysis fallback logic. The script explicitly states it is not the primary understanding engine and only performs lightweight extraction to produce hints for downstream search conditions. There is no evidence of campaign orchestration, creator retrieval, ranking models, outbound messaging, inbox access, or reply generation. This is a material description-behavior mismatch in primary purpose and implemented capabilities.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
94% confidence
Finding

The supplied code chunk is narrowly focused on product resolution and product understanding, not the full outreach workflow claimed in the description. It fetches product URLs, extracts product signals, analyzes product details, assesses page quality, and returns structured product summaries or fallback requests for more user input. There is no evidence here of creator discovery, ranking, email generation, send operations, inbox access, or reply assistance. While product understanding is one component of the declared description, the declared purpose materially overstates the capability of this code chunk, so the description does not accurately represent what this code actually does.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.