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Security audit

Local Whisper

Security checks for vulnerabilities and agentic risk

Overview

The skill claims private local transcription, but its packaged code can automatically send audio to OpenAI or Groq and exposes an unauthenticated local daemon.

Review carefully before installing. Do not rely on the current package as local-only unless cloud dependencies and auto cloud fallback are removed or disabled. Avoid loading the LaunchAgent plist unless you have audited the exact plist and know how to unload it. Run the daemon only in a trusted local environment, and prefer an authenticated or upload-only design that does not accept arbitrary filesystem paths.

Vulnerability Patterns
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs
  • System PersistenceInstalls backdoors, hooks, services, or scheduled tasks that survive the run
  • 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
Findings (6)

T09 · Insecure Skill Coding Practices

Error
Location
scripts/transcribe_large.sh:42
Finding
Python Code Injection Through an Unsafely Interpolated Audio Path<![CDATA[ ## Vulnerability Details **File Location**: `scripts/transcribe_large.sh:42-49` **Vulnerability Type**: Python code injection through heredoc interpolation **Risk Level**: High ### Vulnerable Code ```bash /usr/bin/python3 << EOF import sys sys.path.insert(0, "$SCRIPT_DIR") from transcriber import Transcriber translation_mode = $([[ -n "$TRANSLATE" ]] && echo "True" || echo "False") t = Transcriber(backend='mlx', model='distil-large-v3', translation_mode=translation_mode) result = t.transcribe("$AUDIO_FILE") print(result) EOF ``` ### Technical Analysis `AUDIO_FILE` and `SCRIPT_DIR` are expanded by the shell directly into Python source code. Although the variables appear between Python quotation marks, their contents are not escaped according to Python string-literal rules. A crafted filename containing quotation marks, newlines, backslashes, or valid Python syntax can terminate the string passed to `t.transcribe()` and inject additional Python statements. The initial shell file-existence check does not prevent exploitation because macOS files can contain characters that are significant in Python source. This is not conventional shell command injection; it is source-code generation followed by execution by `/usr/bin/python3`. ### Attack Path 1. An attacker creates or supplies an audio file with a filename containing Python string-termination characters and additional Python syntax. 2. The victim invokes `scripts/transcribe_large.sh` with that file. 3. The shell confirms that the crafted path refers to an existing file. 4. The path is interpolated into the unquoted heredoc. 5. Python parses the attacker-controlled portion as source code. 6. The injected code executes with the same user privileges as the transcription process. ### Impact Assessment Successful exploitation permits arbitrary Python execution under the invoking user account. The injected code could read or modify user-accessible files, execute local commands, access environment var ...[truncated 206 chars]
Remediation
<![CDATA[ ## Remediation Suggestions Do not embed paths or other externally controlled values in generated Python source. Pass them as arguments to Python and quote the heredoc delimiter: ```bash /usr/bin/python3 - "$SCRIPT_DIR" "$AUDIO_FILE" "$TRANSLATE" <<'PY' import sys script_dir = sys.argv[1] audio_file = sys.argv[2] translation_mode = sys.argv[3] == "--translate" sys.path.insert(0, script_dir) from transcriber import Transcriber transcriber = Transcriber( backend="mlx", model="distil-large-v3", translation_mode=translation_mode, ) print(transcriber.transcribe(audio_file)) PY ``` Alternatively, replace the shell-generated Python entirely with `transcriber_cli.py`. Add regression tests using filenames containing quotation marks, backslashes, spaces, Unicode characters, dollar signs, and newlines. ]]>

T05 · Unauthorized Access and Privilege Escalation

Error
Location
scripts/daemon.py:120
Finding
Unauthenticated Daemon Accepts Arbitrary Local Filesystem Paths<![CDATA[ ## Vulnerability Details **File Location**: `scripts/daemon.py:120-167` **Vulnerability Type**: Missing authorization and unrestricted local-file access **Risk Level**: High ### Vulnerable Code ```python if 'application/json' in content_type: body = self.rfile.read(content_length) data = json.loads(body) audio_path = data.get('file') or data.get('path') language = data.get('language') translate = data.get('translate', False) if not audio_path: self.send_json({"error": "Missing 'file' or 'path' in JSON"}, 400) stats["failed"] += 1 return if not os.path.exists(audio_path): self.send_json({"error": f"File not found: {audio_path}"}, 400) stats["failed"] += 1 return stats["total_audio_bytes"] += os.path.getsize(audio_path) # ... text = transcriber.transcribe(audio_path, language=language) ``` The server is exposed without request authentication: ```python server = HTTPServer(('127.0.0.1', args.port), WhisperHandler) ``` ### Technical Analysis The loopback binding prevents direct remote-network access, but it does not establish authorization between local applications. Any process able to reach `127.0.0.1:8787` can submit an absolute or relative path. The daemon validates only that the path exists. It does not: - Require an authentication token. - Restrict paths to an approved media directory. - Canonicalize the path before enforcing policy. - Reject symbolic links. - Require a regular file. - Confirm that the requester is authorized to access the selected file. The selected path is opened by the daemon under the daemon user's privileges. Consequently, a less-trusted local process can use the persistent service as a confused deputy to process files available to the daemon account. ### Attack Path 1. The victim starts the daemon, potentially through the documented login persistence mechanism. 2. A local process connects to `127.0.0.1:8787`. 3. The proce ...[truncated 833 chars]
Remediation
<![CDATA[ ## Remediation Suggestions Prefer removing path-based requests and accepting uploaded audio bytes only. If path-based requests are required: 1. Generate a cryptographically random bearer token when installing or starting the daemon. 2. Require that token on every transcription and status request. 3. Resolve the path with `os.path.realpath()` and restrict it to explicitly configured media roots. 4. Use `os.lstat()` to reject symbolic links and require a regular file. 5. Consider a Unix-domain socket with restrictive filesystem permissions instead of a TCP port. 6. Run the daemon under a dedicated, minimally privileged account where operationally practical. 7. Avoid returning full local paths in error responses. 8. Document the local trust boundary and authentication requirements. ]]>

T06 · System Persistence

Warning
Location
README.md:35
Finding
Optional Login Persistence Uses an Unavailable and Unaudited LaunchAgent Definition<![CDATA[ ## Vulnerability Details **File Location**: `README.md:35-39` and `SKILL.md:102-106` **Vulnerability Type**: Persistent login-service registration **Risk Level**: Medium ### Vulnerable Code ```bash ## Auto-Start ```bash cp com.local-whisper.plist ~/Library/LaunchAgents/ launchctl load ~/Library/LaunchAgents/com.local-whisper.plist ``` ``` The equivalent instructions in `SKILL.md` are: ```bash cp com.local-whisper.plist ~/Library/LaunchAgents/ launchctl load ~/Library/LaunchAgents/com.local-whisper.plist ``` ### Technical Analysis The instructions ask the user to copy a property-list definition into `~/Library/LaunchAgents` and load it through `launchctl`. A LaunchAgent survives the immediate Skill invocation and can run automatically in later login sessions. Keeping a transcription model loaded may justify an optional user-level service, but it is not necessary for one-shot transcription and therefore exceeds the minimum privileges and lifetime required for the base function. The referenced `com.local-whisper.plist` is absent from the audited project. Its executable path, arguments, environment, restart policy, file permissions, and log destinations therefore cannot be verified. As packaged, the instructions fail unless the user obtains or creates the file separately. Loading an unreviewed replacement could persist arbitrary commands. ### Attack Path 1. The user follows the auto-start documentation. 2. Because the referenced plist is missing, the user obtains or creates one from an external or unreviewed source. 3. The file is copied into the user's LaunchAgents directory. 4. `launchctl load` registers and starts the configured program. 5. The configured process executes at login and persists across sessions with the user's privileges. If the plist starts this daemon, the unauthenticated local endpoint and its file-processing attack surface also remain continuously available. ### Impact Assessment A legitimate plist would establish user-l ...[truncated 395 chars]
Remediation
<![CDATA[ ## Remediation Suggestions 1. Include the exact plist in the repository so it can be reviewed. 2. Use absolute paths to a trusted virtual environment and project script. 3. Ensure the plist and executable are owned by the user and are not writable by untrusted users. 4. Make auto-start explicitly optional and explain why it is needed. 5. Provide complete removal instructions, for example unloading the agent and deleting the plist. 6. Prefer modern `launchctl bootstrap` and `bootout` commands where appropriate. 7. Avoid broad environment inheritance or unnecessary restart policies. 8. Document that manual daemon startup or direct CLI transcription avoids cross-session persistence. ]]>

T09 · Insecure Skill Coding Practices

Warning
Location
scripts/daemon.py:104
Finding
Unbounded HTTP Request Body Can Exhaust Memory, Disk, and Processing Resources<![CDATA[ ## Vulnerability Details **File Location**: `scripts/daemon.py:104-159` **Vulnerability Type**: Missing request-size and resource limits **Risk Level**: Medium ### Vulnerable Code ```python # Get content length content_length = int(self.headers.get('Content-Length', 0)) if content_length == 0: self.send_json({"error": "No audio data"}, 400) stats["failed"] += 1 return # Check content type content_type = self.headers.get('Content-Type', '') # ... else: audio_data = self.rfile.read(content_length) stats["total_audio_bytes"] += len(audio_data) # Parse query params for options query = parse_qs(urlparse(self.path).query) language = query.get('language', [None])[0] translate = query.get('translate', ['false'])[0].lower() == 'true' # Write to temp file ext = '.wav' if 'audio/ogg' in content_type or 'audio/opus' in content_type: ext = '.ogg' elif 'audio/mpeg' in content_type or 'audio/mp3' in content_type: ext = '.mp3' elif 'audio/m4a' in content_type: ext = '.m4a' with tempfile.NamedTemporaryFile(suffix=ext, delete=False) as f: f.write(audio_data) audio_path = f.name ``` ### Technical Analysis The daemon trusts the client-supplied `Content-Length` and reads that amount into a single in-memory byte string. There is no maximum upload size, streaming limit, request timeout, audio-duration limit, or per-client rate limit. The server uses Python's single-threaded `HTTPServer`. A client that sends a large or deliberately slow body can occupy the only request-handling thread. A completed large body is duplicated through memory and temporary storage before being passed to computationally expensive transcription code. Temporary files are deleted only on the successful path. If parsing or transcription throws an exception, the cleanup block is skipped and uploaded audio can remain in temporary storage. ### Attack Path 1. A local process connects ...[truncated 771 chars]
Remediation
<![CDATA[ ## Remediation Suggestions 1. Reject missing, negative, malformed, or excessive `Content-Length` values before reading. 2. Set a conservative configurable maximum upload size. 3. Stream request data into a temporary file in bounded chunks instead of retaining the complete body in memory. 4. Set socket and request timeouts. 5. Enforce decoded-audio duration and format limits before transcription. 6. Add rate limiting or request concurrency controls. 7. Move temporary-file deletion into a `finally` block. 8. Use a dedicated private temporary directory with restrictive permissions. 9. Consider a production-grade local HTTP framework with explicit body-size and timeout controls. ]]>

T09 · Insecure Skill Coding Practices

Error
Location
scripts/transcriber.py:112
Finding
Automatic Backend Selection Can Upload Audio Despite Local-Only Privacy Claims<![CDATA[ ## Vulnerability Details **File Location**: `scripts/transcriber.py:112-118` and `scripts/transcriber.py:267-283` **Vulnerability Type**: Unexpected sensitive-data transmission to cloud services **Risk Level**: High ### Vulnerable Code ```python if backend == "auto": if MLX_AVAILABLE: backend = "mlx" elif OPENAI_AVAILABLE and os.getenv('OPENAI_API_KEY'): backend = "openai" elif GROQ_AVAILABLE and os.getenv('GROQ_API_KEY'): backend = "groq" elif FASTER_WHISPER_AVAILABLE: backend = "local" else: raise ValueError("No transcription backend available. Install dependencies.") ``` The selected API backend uploads the opened file: ```python with open(audio_file_path, 'rb') as audio_file: if self.translation_mode and self.backend == 'openai': response = self.client.audio.translations.create( model=self.model, file=audio_file, prompt=prompt, timeout=self.timeout ) else: response = self.client.audio.transcriptions.create( model=self.model, file=audio_file, language=language, prompt=prompt, timeout=self.timeout ) ``` The documentation states: ```markdown - ✅ Private (audio never leaves your Mac) - ✅ Works offline ``` ### Technical Analysis The daemon and CLI default to the `auto` backend. If MLX is unavailable but the OpenAI package and `OPENAI_API_KEY` are present, OpenAI is selected before the local `faster-whisper` backend. Groq is similarly selected before the local CPU backend when its credentials exist. The audio file is then opened and sent through the provider SDK. This behavior conflicts with the Skill's central claims that audio never leaves the Mac and that transcription is local and offline. Installing the documented requirements makes both cloud SDKs available. Consequently, a pre-existing API key can change the privacy properties withou ...[truncated 1030 chars]
Remediation
<![CDATA[ ## Remediation Suggestions 1. Change `auto` so it considers only local backends, preferably MLX followed by `faster-whisper`. 2. Require explicit `--backend openai` or `--backend groq` selection before any cloud transmission. 3. Display a clear warning that audio will leave the machine when a cloud backend is selected. 4. Separate cloud integrations into optional dependency groups so local installation does not install them. 5. Update the documentation to accurately distinguish local and cloud modes. 6. Expose the selected backend prominently at startup and reject accidental cloud fallback. 7. Add tests confirming that `backend="auto"` never creates an OpenAI or Groq client. ]]>

T08 · Insecure Dependencies

Warning
Location
requirements.txt:4
Finding
Unpinned Dependencies and Unnecessary Cloud SDKs Increase Supply-Chain Exposure<![CDATA[ ## Vulnerability Details **File Location**: `requirements.txt:4-18` **Vulnerability Type**: Non-reproducible third-party dependency installation **Risk Level**: Medium ### Vulnerable Code ```text # Core python-dotenv>=1.0.0 # OpenAI Whisper API openai>=1.12.0 # Groq API (optional - fast & cheap cloud) groq>=0.4.0 # Local faster-whisper (optional - CPU-based) faster-whisper>=1.0.0 # MLX Lightning Whisper (Apple Silicon - fastest local option) # Only works on macOS with M1/M2/M3/M4 lightning-whisper-mlx>=0.0.10; sys_platform == "darwin" and platform_machine == "arm64" ``` The installation instruction is: ```bash pip3 install -r requirements.txt ``` ### Technical Analysis Every dependency uses a lower-bound constraint without an upper bound, exact version, lockfile, or package hash. A future direct or transitive release can therefore be selected without review, causing different installations to execute materially different package code. The project is marketed as local transcription, yet the default dependency installation includes OpenAI and Groq SDKs. This unnecessarily enlarges the dependency graph and also enables the automatic cloud-backend behavior when credentials are present. The audited names correspond to known package names; the available evidence does not establish dependency confusion, typosquatting, or an intentionally malicious package. The finding concerns unsafe version policy and avoidable supply-chain exposure. ### Attack Path 1. The user runs the documented pip installation command. 2. Pip resolves the newest versions satisfying the lower bounds. 3. A compromised, malicious, or unexpectedly incompatible eligible direct or transitive release is selected. 4. Package build, installation, import, or runtime code executes in the user's Python environment. 5. The package receives the privileges and data available to the transcription process. ### Impact Assessment A compromised dependency could execute code with the installi ...[truncated 399 chars]
Remediation
<![CDATA[ ## Remediation Suggestions 1. Pin reviewed direct and transitive dependency versions in a lockfile. 2. Generate and enforce package hashes, such as with `pip-compile --generate-hashes`. 3. Separate local, OpenAI, and Groq dependencies into optional extras. 4. Keep the default installation limited to dependencies required for the declared local functionality. 5. Install into an isolated virtual environment rather than the global user Python environment. 6. Use automated vulnerability and provenance scanning for locked dependencies. 7. Review lockfile updates before release and document the model-download sources used by transcription libraries. ]]>
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Rogue AgentSelf-Modification, Session Persistence
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
Findings (31)

Tp4

High
Category
MCP Tool Poisoning
Confidence
89% confidence
Finding
The claimed scope is a private local Apple-Silicon MLX Whisper skill, but the finding indicates backend and model selection beyond that scope, including non-local/cloud options. This broader-than-declared behavior is dangerous because users may deploy it under false assumptions about privacy boundaries, platform restrictions, and where their data is processed.

Tp4

High
Category
MCP Tool Poisoning
Confidence
88% confidence
Finding
The claimed scope is a private local Apple-Silicon MLX Whisper skill, but the finding indicates backend and model selection beyond that scope, including non-local/cloud options. This broader-than-declared behavior is dangerous because users may deploy it under false assumptions about privacy boundaries, platform restrictions, and where their data is processed.

Tp4

High
Category
MCP Tool Poisoning
Confidence
97% confidence
Finding
The claimed scope is a private local Apple-Silicon MLX Whisper skill, but the finding indicates backend and model selection beyond that scope, including non-local/cloud options. This broader-than-declared behavior is dangerous because users may deploy it under false assumptions about privacy boundaries, platform restrictions, and where their data is processed.

Tp4

High
Category
MCP Tool Poisoning
Confidence
94% confidence
Finding
The claimed scope is a private local Apple-Silicon MLX Whisper skill, but the finding indicates backend and model selection beyond that scope, including non-local/cloud options. This broader-than-declared behavior is dangerous because users may deploy it under false assumptions about privacy boundaries, platform restrictions, and where their data is processed.

Description-Behavior Mismatch

High
Confidence
98% confidence
Finding
The file-level documentation explicitly advertises cloud backends (OpenAI and Groq) even though the skill metadata promises private local transcription with no API costs. This mismatch is dangerous because users may rely on the manifest’s privacy claims while audio can in fact be routed to third-party services, creating confidentiality and billing risk.

Description-Behavior Mismatch

High
Confidence
98% confidence
Finding
The automatic backend selection falls back to OpenAI or Groq whenever their packages and API keys are present, despite the skill being described as local and private. In environments where these credentials are set, users can unknowingly have audio sent off-device and incur charges, directly violating expected trust and privacy boundaries.

Session Persistence

Medium
Category
Rogue Agent
Content
## Auto-Start

```bash
cp com.local-whisper.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.local-whisper.plist
```
Confidence
75% confidence
Finding
Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Session Persistence

Medium
Category
Rogue Agent
Content
## Auto-Start

```bash
cp com.local-whisper.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.local-whisper.plist
```
Confidence
75% confidence
Finding
Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Session Persistence

Medium
Category
Rogue Agent
Content
## Auto-Start

```bash
cp com.local-whisper.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.local-whisper.plist
```
Confidence
75% confidence
Finding
Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Session Persistence

Medium
Category
Rogue Agent
Content
## Auto-Start

```bash
cp com.local-whisper.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.local-whisper.plist
```
Confidence
75% confidence
Finding
Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Session Persistence

Medium
Category
Rogue Agent
Content
```bash
cp com.local-whisper.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.local-whisper.plist
```

## Requirements
Confidence
75% confidence
Finding
Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Session Persistence

Medium
Category
Rogue Agent
Content
```bash
cp com.local-whisper.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.local-whisper.plist
```

## Requirements
Confidence
75% confidence
Finding
Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Lp3

Medium
Category
MCP Least Privilege
Confidence
93% confidence
Finding
The skill requests users to run shell commands, install Python packages, start a daemon, expose a localhost HTTP service, and configure a CLI command, but it declares no explicit tool scope or permissions. This is dangerous because reviewers and users cannot easily see that the skill needs shell, network, environment, and file-write capabilities, increasing the chance of over-trusting or deploying it in contexts where those capabilities are inappropriate.

Intent-Code Divergence

Medium
Confidence
92% confidence
Finding
The file comments market the package as a local Whisper/MLX solution while simultaneously including cloud API packages. That inconsistency is security-relevant because operators may deploy the skill under the assumption that sensitive voice data remains on-device, when the dependency set supports remote transmission and expands the attack surface.

Description-Behavior Mismatch

Medium
Confidence
95% confidence
Finding
The requirements declare cloud STT client libraries (OpenAI and Groq) even though the skill is described as private and local-only. This creates a real trust-boundary mismatch: downstream code can silently route audio or transcripts to third-party services, defeating users’ privacy expectations and increasing the chance of unintended data exfiltration.

Context-Inappropriate Capability

Medium
Confidence
97% confidence
Finding
The JSON mode allows a caller to supply an arbitrary local file path, and the daemon will verify existence, read its size, and pass it to the transcriber. Because the service listens on localhost without authentication, any local process able to connect can induce reads of arbitrary files and potentially exfiltrate sensitive content if the downstream transcription stack or error messages expose file-derived data.

Natural-Language Policy Violations

Medium
Confidence
88% confidence
Finding
The help text explicitly states that translation mode is 'to English', which imposes a specific output language. The file does not present this as a user choice at request time or document locale selection safeguards, so it can violate language/locale policy expectations.

Description-Behavior Mismatch

Medium
Confidence
93% confidence
Finding
The script claims private local transcription, but its default behavior is to contact a daemon over HTTP first and send metadata about the user-supplied audio file, including its absolute path. Even if the file contents are not uploaded by this script, transmitting local filesystem paths to a network service can leak sensitive host information and violates the stated privacy expectation, especially if the daemon is remote via environment configuration.

External Transmission

Medium
Category
Data Exfiltration
Content
)
    
    local response
    response=$(curl -s -X POST "$DAEMON_URL/transcribe" \
        -H "Content-Type: application/json" \
        -d "$json_payload" \
        --connect-timeout 2 \
Confidence
86% confidence
Finding
This curl invocation performs external transmission to an endpoint controlled by an environment variable, so the destination may be non-local even though the default is localhost. In the context of a speech-to-text skill marketed as local and private, unguarded network transmission increases the risk of privacy leakage and surprising data flow beyond what users expect.

Missing User Warnings

Medium
Confidence
91% confidence
Finding
The script sends the absolute audio file path to a daemon over plain HTTP without any explicit runtime warning or consent flow. This can expose usernames, directory names, project names, or other sensitive path elements to another process or host, and if CLAWD_WHISPER_URL is changed from localhost, the disclosure extends off-device contrary to the skill's privacy-focused description.

Intent-Code Divergence

Medium
Confidence
92% confidence
Finding
The script usage and argument parsing indicate support for both `--translate` and `--language`, suggesting those options affect the transcription request. In the embedded Python code, only `translation_mode` is used and the parsed `LANGUAGE` value is never passed to `Transcriber.transcribe`, so the documented language option does nothing.

Intent-Code Divergence

Medium
Confidence
84% confidence
Finding
The top-level docstring presents the module as a transcription component while enumerating OpenAI and Groq cloud backends. In the context of a manifest advertising private local Apple-Silicon transcription with no API costs, this documentation reflects an implementation intent that contradicts the claimed skill intent rather than merely omitting details.

Context-Inappropriate Capability

Medium
Confidence
90% confidence
Finding
The skill's stated purpose is local transcription with MLX Whisper on Apple Silicon, but the code checks for OPENAI_API_KEY and GROQ_API_KEY and later uses them to initialize remote clients. Credential handling for third-party cloud services is not justified by a manifest centered on private local processing.

Missing User Warnings

Medium
Confidence
97% confidence
Finding
The API transcription path opens the local audio file and submits it to external transcription services without any user-facing warning or consent mechanism in this code. In the context of a skill advertised as private/local for Telegram and WhatsApp audio, this creates meaningful privacy risk because potentially sensitive voice messages may be exfiltrated to third parties unexpectedly.

Missing User Warnings

Low
Confidence
78% confidence
Finding
Line L047 tells the user to replace their existing `tools.media.audio` config, and the later config block wires all supported voice-message transcription through this local script. While the file explains benefits and model download behavior, it does not explicitly warn that this modifies the user's global transcription pipeline and may affect existing behavior for incoming media.

Static analysis

No suspicious patterns detected.