Spectra Assure
Community
Docs
failRisk: Tampering
Scanned: 7 days ago

torch

Artifact:
Tensors and Dynamic neural networks in Python with strong GPU acceleration
License: Permissive (BSD-3-Clause)
Published: 28 days ago




SAFE Assessment

Compliance

Licenses
No license compliance issues
Secrets
No sensitive information found

Security

Vulnerabilities
No known vulnerabilities detected
Hardening
1 hardening effectiveness issues

Threats

Tampering
1 malware-like behaviors found
Malware
No evidence of malware inclusion

Popularity

1.25B
Total Downloads
Contributor
Declared Dependencies
18.87k
Dependents

Top issues

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 exclusively used by malicious software with the intent to cause harm. When a software package matches behavior traits of malicious software, it becomes flagged by security solutions. It is highly likely that the software package was tampered with by a malicious actor or a rogue insider.

Prevalence in PyPI community

0 packages
found in
Top 100
0 packages
found in
Top 1k
0 packages
found in
Top 10k
26 packages
in community

Next steps

Investigate reported detections.
Investigate your build and release environment for software supply chain compromise.
You should delay the software release until the investigation is completed.
In the case this behavior is intended, rewrite the flagged code without using the malware-like behaviors.

Problem

Security Development Lifecycle (SDL) is a group of enhanced compile-time checks that report common coding mistakes as errors. These checks prevent the use of hard-to-secure string manipulation functions. They enforce static memory access checks, and allow only the use of range-verified string parsing functions. While these checks do not prevent every memory corruption issue by themselves, they do help reduce the likelihood.

Prevalence in PyPI community

4 packages
found in
Top 100
33 packages
found in
Top 1k
174 packages
found in
Top 10k
3639 packages
in community

Next steps

It's highly recommended to enable these checks for all software components used at security boundaries, or those that process user controlled inputs.
To enable these checks, refer to your programming language toolchain documentation.
In Microsoft VisualStudio, you can enable this feature by setting the compiler option /SDL to ON.

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

Security Development Lifecycle (SDL) is a group of enhanced compile-time checks that report common coding mistakes as errors, preventing them from reaching production. These checks minimize the number of security issues by enforcing strict memory access checks. They also prevent the use of hard-to-secure string and memory manipulation functions. To prove the binary has been compiled with these checks enabled, the compiler emits a special debug object. Removing the debug table eliminates this proof. Therefore, this check only applies to binaries that still have their debug tables.

Prevalence in PyPI community

6 packages
found in
Top 100
47 packages
found in
Top 1k
246 packages
found in
Top 10k
6306 packages
in community

Next steps

You should keep the debug table to prove that the SDL process has been followed.
To enable these checks, refer to your programming language toolchain documentation.
In Microsoft VisualStudio, you can enable this feature by setting the compiler option /SDL to ON.

Problem

Security Development Lifecycle (SDL) is a group of enhanced compile-time checks that report common coding mistakes as errors. These checks prevent the use of hard-to-secure memory manipulation functions. They enforce static memory access checks, and allow only the use of range-verified memory access functions. While these checks do not prevent every memory corruption issue by themselves, they do help reduce the likelihood.

Prevalence in PyPI community

2 packages
found in
Top 100
39 packages
found in
Top 1k
189 packages
found in
Top 10k
4600 packages
in community

Next steps

It's highly recommended to enable these checks for all software components used at security boundaries, or those that process user controlled inputs.
To enable these checks, refer to your programming language toolchain documentation.
In Microsoft VisualStudio, you can enable this feature by setting the compiler option /SDL to ON.

Top behaviors

Prevalence in PyPI community

Behavior commonly used by malicious software (Important)
Behavior uncommon for this community (Uncommon)
1 packages
found in
Top 100
2 packages
found in
Top 1k
9 packages
found in
Top 10k
5667 packages
in community

Prevalence in PyPI community

Behavior uncommon for this community (Uncommon)
0 packages
found in
Top 100
13 packages
found in
Top 1k
82 packages
found in
Top 10k
1822 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
34 packages
found in
Top 100
185 packages
found in
Top 1k
1062 packages
found in
Top 10k
25867 packages
in community

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

Top vulnerabilities

No vulnerabilities found.