Spectra Assure
Community
failIncident: Malware
Scanned: 5 days ago

qdatainstaller

Artifact:
latest
malicious
مكتبة Python لتحميل وتنفيذ الملفات
License: Permissive (MIT)
New!
Published: 5 days 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
1 suspicious application behaviors
Malware
1 malicious components found

INCIDENTS FOR THIS VERSION:

malware
5 days agoReported By: ReversingLabs (Automated)
Learn more about malware detection

Popularity

263
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 files. The detection was made by a machine learning model. This malware detection method is considered proactive, and can typically identify the malware threat type. 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 components to be detected as malicious.

Prevalence in PyPI community

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

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.
Avoid using this software package until it is vetted as safe.
Consider rewriting code that may have triggered the detection due to its malware similarity.

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

Attackers commonly hide their malicious payloads in layers of packing and code obfuscation. Base-encoding is a common data transformation technique used to convert binary payloads into text. Detected software behaviors indicate that the code has the ability to decode and execute Base-encoded data. While presence of dynamic code execution does not imply malicious intent, all of its uses in a software package should be documented and approved. When a software package has behavior traits similar to malicious software, it may become flagged by security solutions. One example of acceptable use for dynamic Base-encoded data execution is transfer of software components over the network.

Prevalence in PyPI community

1 packages
found in
Top 100
6 packages
found in
Top 1k
18 packages
found in
Top 10k
6.2k packages
in community

Next steps

Investigate reported detections as indicators of software tampering.
Consult Mitre ATT&CK documentation: T1027 - Obfuscated Files or Information.
Consider rewriting the flagged code without using the marked behaviors.

Top behaviors

Prevalence in PyPI community

Behavior uncommon for this community (Uncommon)
Behavior commonly used by malicious software (Important)
1 packages
found in
Top 100
4 packages
found in
Top 1k
8 packages
found in
Top 10k
5.74k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
36 packages
found in
Top 100
281 packages
found in
Top 1k
1802 packages
found in
Top 10k
66.4k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
69 packages
found in
Top 100
506 packages
found in
Top 1k
3612 packages
found in
Top 10k
163.85k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
12 packages
found in
Top 100
43 packages
found in
Top 1k
256 packages
found in
Top 10k
14.42k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
30 packages
found in
Top 100
166 packages
found in
Top 1k
1155 packages
found in
Top 10k
31.66k packages
in community

Top vulnerabilities

No vulnerabilities found.