Scan report

ConardLi / easy-dataset

Vulnerability High

Could an attacker exploit mistakes in this code?

275 risk score

Serious issues. Review the high-severity findings before using it.

High
2
Medium
213
Low
79

Sources: Bandit, Semgrep, pip-audit

Malicious pattern Low

Does this code look like it is trying to cause harm?

1 risk score

Minor issues. Worth a glance, unlikely to be urgent.

High
1
Medium
0
Low
53

Sources: YARA, ClamAV, GuardDog, dependency checker

Findings overview
348 total findings
Likely real
9
Uncertain
283
Likely noise
56

The risk classifier rates every finding for how likely it is to be a real issue. Likely noise still counts, just much less.

What we found, in plain English

This repository has a high vulnerability score of 275.0, which means there are many insecure coding patterns that an attacker could easily exploit to break into your system. While the malicious-pattern risk is very low at 1.0, this suggests the code itself does not appear to be intentionally harmful, but rather just poorly written or outdated. The most serious issues involve using unencrypted HTTP servers instead of HTTPS and allowing Docker containers to run as root users, which could let attackers control sensitive data. Additionally, there are several loose version constraints in your dependencies that might allow malicious updates to slip through if you don't update them regularly. Overall, the code is not malicious but is unsafe enough that it needs immediate attention before being used in a production environment.

Written by a local language model from the findings below. The risk scores come from the classifier, not from this summary.

Detections by engine

  • Semgrep 294

    Pattern-based code analysis · Vulnerability

  • Dependency checker 53

    CVEs, typosquats and risky installs · Supply chain

  • YARA 1

    Malware signature rules · Malicious pattern

Score over time

Overall score each time this repository was scanned.

Findings

Highest severity first. Select a finding to see the code and what it means.

Large result: showing the top 254 of 348 findings (up to 200 per engine, most severe and most likely real first). Totals above include everything.