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Scanned: about 7 hours ago

debugpy

Artifact:
latest
Top 1k
An implementation of the Debug Adapter Protocol for Python
License: Permissive (MIT)
Published: about 2 months ago




SAFE Assessment

Compliance

Licenses
No license compliance issues
Secrets
7 debugging symbols found

Security

Vulnerabilities
No known vulnerabilities detected
Hardening
1 execution hijacking concerns

Threats

Tampering
No evidence of software tampering
Malware
No evidence of malware inclusion

INCIDENTS FOR THIS VERSION:

Popularity

1.28B
Total Downloads
Contributor
Declared Dependencies
569
Dependents

Top issues

Problem

Uniform Resource Locators (URLs) are structured addresses that point to locations and assets on the internet. URLs allow software developers to build complex applications that exchange data with servers that can be hosted in multiple geographical regions. URLs can commonly be found embedded in documentation, configuration files, source code and compiled binaries. One or more embedded URLs were discovered to link to raw files hosted on GitHub. Attackers often abuse popular web services to host malicious payloads. Since code-sharing services URLs are typically allowed by security solutions, using them for payload delivery increases the odds that the malicious code will reach the user. While the presence of code-sharing service locations does not imply malicious intent, all of their uses in a software package should be documented and approved. An increasing number of software supply chain attacks in the open source space leverages the GitHub service to deliver malicious payloads.

Prevalence in PyPI community

33 packages
found in
Top 100
206 packages
found in
Top 1k
1631 packages
found in
Top 10k
63840 packages
in community

Next steps

Investigate reported detections.
If the software should not include these network references, investigate your build and release environment for software supply chain compromise.
You should delay the software release until the investigation is completed, or until the issue is risk accepted.
Consider an alternative delivery mechanism for software packages.

Problem

Program database (PDB) files are typically only used during software development. They contain private debug symbols that make it significantly easier to reverse engineer a closed source application. In some cases, having a program database file is equivalent to having access to the source code. Presence of program databases could indicate that one or more software components have been built using a debug profile, instead of the release.

Prevalence in PyPI community

0 packages
found in
Top 100
3 packages
found in
Top 1k
22 packages
found in
Top 10k
252 packages
in community

Next steps

Remove private debug database files from the software package before you release it.

Problem

Software components contain executable code that performs actions implemented during its development. These actions are called behaviors. In the analysis report, behaviors are presented as human-readable descriptions that best match the underlying code intent. While most behaviors are benign, some are commonly abused by malicious software with the intent to cause harm. When a software package shares behavior traits with malicious software, it may become flagged by security solutions. Any detection from security solutions can cause friction for the end-users during software deployment. While the behavior is likely intended by the developer, there is a small chance this detection is true positive, and an early indication of a software supply chain attack.

Prevalence in PyPI community

0 packages
found in
Top 100
2 packages
found in
Top 1k
7 packages
found in
Top 10k
1009 packages
in community

Next steps

Investigate reported detections.
If the software intent does not relate to the reported behavior, investigate your build and release environment for software supply chain compromise.
You should delay the software release until the investigation is completed, or until the issue is risk accepted.
Consider rewriting the flagged code without using the marked behaviors.

Problem

On Linux, external symbols are resolved via the procedure linkage table (PLT) and the global offset table (GOT). Without any protection, both are writable at runtime and thus leave the executable vulnerable to pointer hijacking - an attack where the function address is overwritten with an address of a malicious function. Pointer hijacking can be mitigated by using full read-only relocations, which instruct the compiler to unify global offset tables into a single read-only table. This requires that all external function symbols are resolved at load-time instead of during execution, and may increase loading time for large programs.

Prevalence in PyPI community

22 packages
found in
Top 100
122 packages
found in
Top 1k
767 packages
found in
Top 10k
16246 packages
in community

Next steps

In most cases, it's recommended to use full read-only relocations (in GCC: -Wl,-z,relro,-z,now).
If the executable load-time is an issue, you should use partial read-only relocations.

Problem

Common compilers often embed source code information into executables for debugging purposes, usually by mapping symbols to source filenames or paths. While this is typically desirable in open-source software and standard tools, that information can be used to determine security weaknesses, code repository layout, trade secrets and similar sensitive information. Such symbols make it easier to reverse-engineer a closed source application.

Prevalence in PyPI community

22 packages
found in
Top 100
122 packages
found in
Top 1k
834 packages
found in
Top 10k
18721 packages
in community

Next steps

Strip out such information in the linking phase by using compiler options like the -s flag in GCC, or in the post-build phase by using the strip tool.

Top behaviors

Prevalence in PyPI community

Behavior often found in this community (Common)
13 packages
found in
Top 100
99 packages
found in
Top 1k
566 packages
found in
Top 10k
19246 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
9 packages
found in
Top 100
94 packages
found in
Top 1k
476 packages
found in
Top 10k
16117 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
8 packages
found in
Top 100
64 packages
found in
Top 1k
281 packages
found in
Top 10k
8972 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
9 packages
found in
Top 100
75 packages
found in
Top 1k
309 packages
found in
Top 10k
9292 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
23 packages
found in
Top 100
112 packages
found in
Top 1k
620 packages
found in
Top 10k
10791 packages
in community

Top vulnerabilities

No vulnerabilities found.