Spectra Assure
Community
Docs
failIncident: Malware
Scanned: 5 days ago

dbgpkg

Artifact:
latest
malicious
Research
Python Debugging Toolkit
License: Permissive (MIT)
Published: 6 months ago


SAFE Assessment

Compliance

Licenses
No license compliance issues
Secrets
No sensitive information found

Security

Vulnerabilities
No known vulnerabilities detected
Hardening
No application hardening issues

Threats

Tampering
2 malware-like behaviors found
Malware
5 supply chain attack artifacts

INCIDENTS:

malware
6 months agoReported By: ReversingLabs (Researcher)
Learn more about malware detection
malware
5 months agoReported By: Community (OpenSSF)

Popularity

397
Total Downloads
Contributor
Declared Dependencies
0
Dependents

Top issues

Problem

Proprietary ReversingLabs malware detection algorithms have determined that the software package contains one or more malicious components. The detection was made by either a static byte signature, software component identity, or a complete file hash. This malware detection method is considered highly accurate, and can typically attribute malware to previously discovered software supply chain attacks. It is common to have multiple supply chain attack artifacts that relate to a single malware incident.

Prevalence in PyPI community

0 packages
found in
Top 100
0 packages
found in
Top 1k
5 packages
found in
Top 10k
13913 packages
in community

Next steps

If the software intent does not relate to malicious behavior, investigate the build and release environment for software supply chain compromise.
Avoid using this software package.

Problem

Threat researchers have manually inspected the software package and determined that it contains one or more malicious files. The detection was made by a hash-based file reputation lookup. This malware detection method is considered highly accurate, and can typically identify the malware family by name.

Prevalence in PyPI community

0 packages
found in
Top 100
0 packages
found in
Top 1k
7 packages
found in
Top 10k
13959 packages
in community

Next steps

Investigate the build and release environment for software supply chain compromise.
Avoid using this software package.

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. Detected threat type matches the behaviors typically exhibited by the backdoor malware profile. Backdoors are commonly used by malicious actors to gain unauthorized access to exposed computer systems over the internet. However, due to high-privilege access requirements, some security solutions may also trigger this detection when analyzed.

Prevalence in PyPI community

0 packages
found in
Top 100
0 packages
found in
Top 1k
1 packages
found in
Top 10k
183 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.
In the case this behavior is intended, rewrite the flagged code without using the malware-like 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. Open source projects are the intellectual property of their respective authors. At any time, the authors may choose to completely remove the software component from a public repository. This often occurs when a software project reaches its end-of-life stage, or when the software authors lose interest in maintaining the project. This kind of removal frees up the software package name, its unique software identifier in the public repository, for other developers to use. However, new software project owners might have malicious intent. Threat actors are continuously monitoring popular package names in case their unique identifiers suddenly become available for hijacking. Once the software projects falls under new ownership, the new maintainers may opt to use the project popularity to spread malware to unsuspecting users.

Prevalence in PyPI community

No prevalence information at this time

Next steps

Inspect behaviors exhibited by the detected software components.
If the software behaviors differ from expected, investigate the build and release environment for software supply chain compromise.
Revise the use of components that raise these alarms. If you can't deprecate those components, make sure that their versions are pinned.
Avoid using this software package until it is vetted as safe.

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

No prevalence information at this time

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

No behavior prevalence information at this time

Prevalence in PyPI community

Behavior uncommon for this community (Uncommon)
1 packages
found in
Top 100
17 packages
found in
Top 1k
104 packages
found in
Top 10k
3113 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
85 packages
found in
Top 100
751 packages
found in
Top 1k
6292 packages
found in
Top 10k
363809 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
100 packages
found in
Top 100
843 packages
found in
Top 1k
7075 packages
found in
Top 10k
339347 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
57 packages
found in
Top 100
456 packages
found in
Top 1k
3002 packages
found in
Top 10k
132844 packages
in community

Top vulnerabilities

No vulnerabilities found.