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SQ20119

Detected digital signatures that rely on a weak digest algorithm for integrity validation.

priorityCI/CD statusseverityeffortSAFE levelSAFE assessment
passlowmediumNoneNone

About the issue​

Digital signatures are applied to applications, packages and documents as a cryptographically secured authenticity record. Signatures verify the origin and the integrity of the object they apply to. The integrity validation relies on the cryptographic strength of the encryption and the hash verification algorithm. If either of the two is considered weak by current standards, there is a chance the signed object could be maliciously modified, without triggering the integrity failure check.

How to resolve the issue​

  • Create signatures with strong ECC key-length of at least 224 bits, or RSA key-length of at least 2048 bits, and use SHA256 as the hashing algorithm. While encryption key-length upgrade does require you to obtain a new certificate, the hashing algorithm can freely be selected during signing.
  • With Microsoft SignTool, you can specify the hashing algorithm using the /fd SHA256 parameter.

Incidence statistics​

ReversingLabs periodically collects and analyzes the contents of popular software package repositories for threat research purposes. Analysis results are used to calculate incidence statistics for issues (policy violations) that Spectra Assure can detect in software packages.

This section is updated when new data becomes available.

Total amount of packages analyzed

  • RubyGems: 183K
  • Nuget: 644K
  • PyPi: 628K
  • NPM: 3.72M

Total detections per repository

For every repository, the chart shows the number of packages that triggered the software assurance policy. In other words, it shows how many packages in each package repository were found to have the specific issue described on this page. This information helps you understand how common the issue is across different software communities.

If a repository is absent from the chart, that means none of the packages in that repository triggered this policy during analysis, or the policy was not used during analysis.

Distribution of total detections by project popularity

For every repository, the chart shows how many of the total detections belong to the Top 100 (1-100), Top 1000 (101-1000) and Top 10 000 (1001-10 000) most downloaded projects. This information helps you understand the impact of the issue within each community, making it clearer when the issue affects the most popular projects.

If the chart shows zero values for all of the top project groups, that means all detections were in unranked projects (lower than 10 000 on the list of most downloaded projects).