List of software quality issues with the number of affected components.
category ALL
Policies
Info
Category
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
2 packages
found in
Top 1k
18 packages
found in
Top 10k
15.82k 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
1 packages
found in
Top 1k
15 packages
found in
Top 10k
15.24k 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 infostealer malware profile. Infostealers are commonly used to steal sensitive user data such as stored login details, financial information, and other personally identifiable information.
Prevalence in PyPI community
0 packages
found in
Top 100
0 packages
found in
Top 1k
6 packages
found in
Top 10k
2.63k 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
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
0 packages
found in
Top 100
7 packages
found in
Top 1k
48 packages
found in
Top 10k
22.29k 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 components sometimes need to interact with sensitive parts of the runtime environment, often requiring privileged access to accomplish a task. Cloud workload configuration includes high-value information such as cloud provider credentials, environment variables containing secrets, and configuration files used by cloud and container orchestration tools. That information is used by applications running in cloud and containerized environments. Attackers often abuse access to cloud workload configuration to harvest secrets from developer machines and build environments, which can later be exfiltrated to attacker-controlled infrastructure and used to gain unauthorized access to cloud resources. While the presence of code that accesses cloud workload configuration does not necessarily imply malicious intent, all of its uses in a software package should be documented and approved. Only select software components should consider using functions that read cloud workload configuration. One example of acceptable use for such functions is infrastructure-as-code tooling that legitimately needs to read credentials to provision or manage cloud resources on behalf of the developer.
Prevalence in PyPI community
5 packages
found in
Top 100
13 packages
found in
Top 1k
83 packages
found in
Top 10k
2.05k packages
in community
Next steps
Investigate reported detections as indicators of software tampering.
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. 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
2 packages
found in
Top 100
12 packages
found in
Top 1k
54 packages
found in
Top 10k
99.05k 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.
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
2 packages
found in
Top 100
22 packages
found in
Top 1k
623 packages
found in
Top 10k
614.08k 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 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
7 packages
found in
Top 100
34 packages
found in
Top 1k
839 packages
found in
Top 10k
928.02k 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
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. Telegram, like many other instant messaging solutions, provides a public-facing API that the chat client can invoke when an appropriate application event occurs. Attackers often abuse Telegram APIs as a command-and-control mechanism that instructs the infected computer systems to perform malicious actions. While the presence of Telegram APIs 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 leverage Telegram infrastructure for command and control.
Prevalence in PyPI community
1 packages
found in
Top 100
2 packages
found in
Top 1k
17 packages
found in
Top 10k
3.51k 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.
Remove all references to flagged network locations.
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
17 packages
found in
Top 100
116 packages
found in
Top 1k
699 packages
found in
Top 10k
27.52k packages
in community
Next steps
Investigate reported detections as indicators of software tampering.
Consult Mitre ATT&CK documentation: T1033 - System Owner/User Discovery.