ClawHub

Survival Curve Risk Table

Security checks across malware telemetry and agentic risk

Overview

This survival-plotting skill mostly does what it claims, but it can load Python pickle files as data, which can execute code if the file is malicious.

Install only in an isolated environment. Do not use .pkl or .pickle input files unless they come from a fully trusted source. Prefer CSV, Excel, or SAS data, and the maintainer should remove pickle support, remove the pil dependency, and pin reviewed dependency versions before broad use.

SkillSpector

By NVIDIA
Vulnerability Patterns
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • 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 (11)

Context-Inappropriate Capability

High
Confidence
98% confidence
Finding
The code accepts .pkl/.pickle input and calls pandas.read_pickle(), which deserializes attacker-controlled Python objects. Pickle deserialization can execute arbitrary code during loading, so a user opening an untrusted dataset could trigger code execution on the host. In a survival-analysis plotting skill, this capability is not necessary and therefore increases risk rather than serving the workflow.

Unpinned Dependencies

Low
Category
Supply Chain
Content
lifelines
matplotlib
numpy
pandas
Confidence
89% confidence
Finding
lifelines

Unpinned Dependencies

Low
Category
Supply Chain
Content
lifelines
matplotlib
numpy
pandas
pil
Confidence
89% confidence
Finding
matplotlib

Unpinned Dependencies

Low
Category
Supply Chain
Content
lifelines
matplotlib
numpy
pandas
pil
pillow
Confidence
93% confidence
Finding
numpy

Unpinned Dependencies

Low
Category
Supply Chain
Content
lifelines
matplotlib
numpy
pandas
pil
pillow
seaborn
Confidence
92% confidence
Finding
pandas

Unpinned Dependencies

Low
Category
Supply Chain
Content
matplotlib
numpy
pandas
pil
pillow
seaborn
Confidence
97% confidence
Finding
pil

Unpinned Dependencies

Low
Category
Supply Chain
Content
numpy
pandas
pil
pillow
seaborn
Confidence
94% confidence
Finding
pillow

Unpinned Dependencies

Low
Category
Supply Chain
Content
pandas
pil
pillow
seaborn
Confidence
88% confidence
Finding
seaborn

Known Vulnerable Dependency: numpy — 10 advisory(ies): CVE-2014-1859 (Numpy arbitrary file write via symlink attack); CVE-2021-41495 (NumPy NULL Pointer Dereference); CVE-2021-33430 (NumPy Buffer Overflow (Disputed)) +7 more

Critical
Category
Supply Chain
Confidence
83% confidence
Finding
numpy

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
90% confidence
Finding
pillow

Possible Typosquatting: 'pil' resembles popular package 'pip'

High
Category
Supply Chain
Confidence
98% confidence
Finding
pil

VirusTotal

VirusTotal findings are pending for this skill version.

View on VirusTotal