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Scanned: 3 days ago

auto-backup-linux

Artifact:
latest
一个用于Linux服务器的自动备份工具,支持文件备份、压缩和上传到云端
License: Permissive (MIT)
New!
Published: 3 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
No evidence of software tampering
Malware
No evidence of malware inclusion

Popularity

751
Total Downloads
Contributor
Declared Dependencies
0
Dependents

Top issues

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

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 anonymous file-sharing services. Attackers often abuse popular web services to host malicious payloads. Since file-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 file-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 anonymous file-sharing services to deliver malicious payloads.

Prevalence in PyPI community

0 packages
found in
Top 100
0 packages
found in
Top 1k
2 packages
found in
Top 10k
619 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

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.

Problem

Operating systems allow multiple user accounts to coexist on a single computer system. Each registered user has identity information associated with their account. At the very least, user accounts consist of a user name and an optional password. In some cases, user account data may also include personally identifiable information. Extended personal information may include user's given and last name, their email and mailing address, personal photo and their telephone number. Financially motivated attackers may seek to collect personal information for purposes of selling the private data to a third-party. Malicious code that typically exhibits these behavior traits is commonly referred to as an information stealer. While the presence of code that accesses identity information does not necessarily imply malicious intent, all of its uses in a software package should be documented and approved. Accessing identity information is a very common behavior for software packages. One example of acceptable use for such functions is verifying that the active user has purchased a software license that allows them to run the application.

Prevalence in PyPI community

16 packages
found in
Top 100
113 packages
found in
Top 1k
669 packages
found in
Top 10k
19.67k packages
in community

Next steps

Investigate reported detections as indicators of software tampering.
Consult Mitre ATT&CK documentation: T1033 - System Owner/User Discovery.

Top behaviors

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 uncommon for this community (Uncommon)
1 packages
found in
Top 100
1 packages
found in
Top 1k
15 packages
found in
Top 10k
199 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
67 packages
found in
Top 100
525 packages
found in
Top 1k
4055 packages
found in
Top 10k
154.83k 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 often found in this community (Common)
9 packages
found in
Top 100
64 packages
found in
Top 1k
343 packages
found in
Top 10k
11.88k packages
in community

Top vulnerabilities

No vulnerabilities found.