ClawReceipt

• Outcome: Pass
• Latest audit: May 29, 2026 · 8:13 PM UTC
• Version: 1.0.0

Overview

ClawReceipt is a local receipt and budget tracker with privacy and dependency-hygiene caveats, but its behavior is coherent and not deceptive.

Install in a trusted virtual environment, consider pinning dependencies before regular use, and treat the SQLite database and exported CSV/XLSX files as private financial records. Review receipt details before letting an agent save them.

SkillSpector

By NVIDIA

Vulnerability Patterns

• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep

Findings (13)

Missing User Warnings

Medium

• Confidence: 88% confidence
• Finding: The README promotes capturing, storing, monitoring, and exporting receipt and budget data, but does not warn users that this is financial information persisted locally in a SQLite database and exportable to CSV/Excel. In an agent-skill context, this omission increases the chance that users or automated systems handle sensitive spending data without understanding retention, disclosure, or filesystem exposure risks.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

textual
rich
pandas
openpyxl

• Confidence: 97% confidence
• Finding: The dependency `textual` is unpinned, so installations may resolve to different versions over time, including newly introduced vulnerable or breaking releases. In a financial receipt-processing skill, supply-chain instability is undesirable because behavior and security posture can change without review.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

textual
rich
pandas
openpyxl
pydantic

• Confidence: 97% confidence
• Finding: The dependency `rich` is unpinned, which creates a reproducibility and supply-chain risk: future installs may pull an unintended version containing security issues or incompatible changes. While not immediately exploitable on its own, this weakens deployment integrity.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

textual
rich
pandas
openpyxl
pydantic
pillow

• Confidence: 99% confidence
• Finding: `pandas` is unpinned, meaning the environment may install any available release, including vulnerable ones. Because this skill handles receipt and spreadsheet-style financial data, a data-processing library with uncontrolled version selection increases the chance of pulling a version with parser or deserialization flaws.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

textual
rich
pandas
openpyxl
pydantic
pillow
python-dotenv

• Confidence: 99% confidence
• Finding: `openpyxl` is unpinned, which is risky for a skill likely to import/export spreadsheet data. Spreadsheet/XML-parsing libraries have a history of security issues, so leaving the version unconstrained increases exposure to vulnerable releases.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

rich
pandas
openpyxl
pydantic
pillow
python-dotenv

• Confidence: 98% confidence
• Finding: `pydantic` is unpinned, so deployments may receive versions with known denial-of-service or validation-related issues. Since this skill likely validates structured receipt/expense data, unsafe version drift can directly affect parsing safety and reliability.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

pandas
openpyxl
pydantic
pillow
python-dotenv

• Confidence: 99% confidence
• Finding: `pillow` is unpinned, which is especially dangerous because image-processing libraries frequently receive critical fixes. This receipt-processing skill likely handles uploaded receipt images, so unconstrained installation can expose the system to image parsing vulnerabilities with serious impact.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

openpyxl
pydantic
pillow
python-dotenv

• Confidence: 96% confidence
• Finding: `python-dotenv` is unpinned, allowing version drift and possible exposure to future or past vulnerable behavior. Although lower risk than parsing libraries, it still affects environment and configuration handling, which is security-sensitive.

Known Vulnerable Dependency: pandas — 1 advisory(ies): CVE-2020-13091 (** DISPUTED ** pandas through 1.0.3 can unserialize and execute commands from an)

High

• Category: Supply Chain
• Confidence: 82% confidence
• Finding: The requirements file specifies `pandas` without a version, so it may resolve to versions affected by published advisories, including the disputed CVE noted by the scanner. Even if the cited advisory is disputed, the absence of version constraints means vulnerable pandas releases cannot be ruled out, and this skill processes financial tabular data where pandas is plausibly used.

Known Vulnerable Dependency: openpyxl — 2 advisory(ies): CVE-2017-5992 (Improper Restriction of XML External Entity Reference in Openpyxl); CVE-2017-5992 (Openpyxl 2.4.1 resolves external entities by default, which allows remote attack)

High

• Category: Supply Chain
• Confidence: 95% confidence
• Finding: `openpyxl` has known XXE-related advisories in older releases, and the unpinned dependency means installations could select an affected version. In a receipt/expense tool that may ingest spreadsheet files, XXE in XML-based workbook parsing can lead to file disclosure, SSRF, or other parser abuse.

Known Vulnerable Dependency: pydantic — 3 advisory(ies): CVE-2021-29510 (Use of "infinity" as an input to datetime and date fields causes infinite loop i); CVE-2024-3772 (Pydantic regular expression denial of service); CVE-2021-29510 (Pydantic is a data validation and settings management using Python type hinting.)

High

• Category: Supply Chain
• Confidence: 94% confidence
• Finding: `pydantic` has multiple published advisories affecting certain versions, including denial-of-service conditions, and the package is unpinned here. Because this skill likely validates user- or file-derived receipt data, an affected pydantic version could allow resource exhaustion or parsing instability.

Known Vulnerable Dependency: pillow — 10 advisory(ies): CVE-2016-2533 (Pillow buffer overflow in ImagingPcdDecode); CVE-2023-50447 (Arbitrary Code Execution in Pillow); CVE-2021-27922 (Pillow Uncontrolled Resource Consumption) +7 more

Critical

• Category: Supply Chain
• Confidence: 98% confidence
• Finding: `pillow` has a long history of severe vulnerabilities, including resource exhaustion and in some versions arbitrary code execution, and this requirements file does not constrain it to a safe release. Given the skill's purpose of extracting receipt information, image ingestion is core functionality, making an unpinned potentially vulnerable image parser particularly dangerous.

Known Vulnerable Dependency: python-dotenv — 1 advisory(ies): CVE-2026-28684 (python-dotenv: Symlink following in set_key allows arbitrary file overwrite via )

Low

• Category: Supply Chain
• Confidence: 71% confidence
• Finding: `python-dotenv` is unpinned, so an affected version could be installed, including versions implicated by the cited symlink-following advisory. This is less central to the receipt-processing attack surface than image or spreadsheet parsing, but it still matters because configuration file writes can become arbitrary file overwrite risks in certain workflows.

VirusTotal

64/64 vendors flagged this skill as clean.

Static analysis

No suspicious patterns detected.