Spectra Assure
Community
failRisk: Secrets
Scanned: 2 days ago

codefrequencychecker

Artifact:
latest
License: unknown
New!
Published: 2 days ago



SAFE Assessment

Compliance

Licenses
No license compliance issues
Secrets
1 active web service credentials

Security

Vulnerabilities
No known vulnerabilities detected
Hardening
No application hardening issues

Threats

Tampering
1 suspicious application behaviors
Malware
No evidence of malware inclusion

Popularity

N/A
Total Downloads
Contributor
Declared Dependencies
0
Dependents

Top issues

Problem

Software as a Service (SaaS) platforms expose programmable interfaces to their authenticated users. These web services enable action automation and secure exchange of information. For authorization, users provide a unique token that confirms their access rights to the web service. Access tokens for supported web services are automatically validated via the least privilege APIs the service exposes. Detected tokens have been accepted as valid by the services they are associated with. This indicates they are currently active and may be abused if exposed to the public. Web service access tokens are considered secrets. They should never be included in a software release package, even if they are obfuscated by encryption on the client-side.

Prevalence in PyPI community

0 packages
found in
Top 100
0 packages
found in
Top 1k
2 packages
found in
Top 10k
248 packages
in community

Next steps

You should securely store web service access tokens, and fully automate their management and periodic rotation.
If tokens were published unintentionally and the software has been made public, you should revoke exposed tokens and file a security incident.
Examples of service tokens that may have been detected include AWS, Facebook, JWT, SWT, Slack and others.

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. Python Package Index (PyPI) repository is often abused by threat actors to publish software packages that exhibit malicious behaviors. Malware authors use numerous tactics to lure developers into including malicious PyPI packages in their software projects. Most malicious packages published on PyPI target developers and their workstations. However, some are designed to activate only when deployed in the end-user environment. Both types of Python malicious packages are detected by proprietary ReversingLabs threat hunting algorithms. This detection method is considered proactive, and it is based on Machine Learning (ML) algorithms that can detect novel malware. The detection is strongly influenced by behaviors that software components exhibit. Behaviors similar to previously discovered malware and software supply chain attacks may cause some otherwise benign software packages to be detected by this policy.

Prevalence in PyPI community

1 packages
found in
Top 100
18 packages
found in
Top 1k
104 packages
found in
Top 10k
16.5k 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

Software developers use programming and design knowledge to build reusable software components. Software components are the basic building blocks for modern applications. Software consumed by an enterprise consists of hundreds, and sometimes even thousands of open source components. Software developers publish components they have authored to public repositories. While a new software project is a welcome addition to the open source community, it is not always prudent to indiscriminately use the latest components when building a commercial application. Irrespective of the software quality, the danger of being the first to try out a new project lies in the fact that the software component may contain novel, currently undetected malicious code. Therefore, it is prudent to review software component behaviors and even try out software component in a sandbox, an environment meant for testing untrusted code.

Prevalence in PyPI community

0 packages
found in
Top 100
0 packages
found in
Top 1k
5 packages
found in
Top 10k
38.63k packages
in community

Next steps

Check the software component behaviors for anomalies.
Consider exploratory software component testing within a sandbox environment.
Consider replacing the software component with a more widely used alternative.
Avoid using this software package until it is vetted as safe.

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

20 packages
found in
Top 100
92 packages
found in
Top 1k
907 packages
found in
Top 10k
43.88k 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

Software developers use programming and design knowledge to build reusable software components. Software components are the basic building blocks for modern applications. Software consumed by an enterprise consists of hundreds, and sometimes even thousands of open source components. Software developers publish components they have authored to public repositories. While a new software project is a welcome addition to the open source community. it is not always prudent to indiscriminately use the latest components when building a commercial application. Irrespective of the software quality, the danger of using components that are rarely used to build applications lies in the fact that the software component may contain novel, currently undetected malicious code. Therefore, it is prudent to review software component behaviors and even try out software component in a sandbox, an environment meant for testing untrusted code.

Prevalence in PyPI community

1 packages
found in
Top 100
11 packages
found in
Top 1k
910 packages
found in
Top 10k
717.09k packages
in community

Next steps

Check the software component behaviors for anomalies.
Consider exploratory software component testing within a sandbox environment.
Consider replacing the software component with a more widely used alternative.
Avoid using this software package until it is vetted as safe.

Top behaviors

Prevalence in PyPI community

Behavior uncommon for this community (Uncommon)
0 packages
found in
Top 100
15 packages
found in
Top 1k
156 packages
found in
Top 10k
5.53k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
39 packages
found in
Top 100
252 packages
found in
Top 1k
1477 packages
found in
Top 10k
53.15k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
7 packages
found in
Top 100
57 packages
found in
Top 1k
383 packages
found in
Top 10k
18.3k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
68 packages
found in
Top 100
533 packages
found in
Top 1k
3858 packages
found in
Top 10k
146.15k packages
in community

Prevalence in PyPI community

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

Top vulnerabilities

No vulnerabilities found.