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Scanned: 4 days ago

uvloop

Artifact:
latest
Top 1k
Fast implementation of asyncio event loop on top of libuv
License: Permissive (MIT)
Published: about 2 months ago




SAFE Assessment

Compliance

Licenses
No license compliance issues
Secrets
2 debugging symbols found

Security

Vulnerabilities
No known vulnerabilities detected
Hardening
1 execution hijacking concerns

Threats

Tampering
No evidence of software tampering
Malware
No evidence of malware inclusion

Popularity

1.1B
Total Downloads
Contributors
Declared Dependencies
1.28k
Dependents

Top issues

Problem

Operating systems execute application code in multiple privilege access levels. Separation of privileges is designed to protect the stability and integrity of the operating system by shielding it from issues that the user run applications may cause. However, some users may need to interact with higher privilege parts of the operating system to accomplish specific tasks. For this purpose, operating systems provide facilities that users may leverage to temporarily elevate their running privileges. Users with higher privileges can run any application with the same privilege level as their own. Attackers often try to trick privileged users into running malicious code, enabling them to infect the operating system. While the presence of code that elevates user privileges does not necessarily imply malicious intent, all of its uses in a software package should be documented and approved. Only select applications should consider using functions that can elevate user privileges. One example of acceptable use for such functions is allowing the users to install software packages and updates.

Prevalence in PyPI community

1 packages
found in
Top 100
17 packages
found in
Top 1k
111 packages
found in
Top 10k
2407 packages
in community

Next steps

Investigate reported detections as indicators of software tampering.
Consult Mitre ATT&CK documentation: T1548 - Abuse Elevation Control Mechanism.
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 raw files hosted on GitHub. Attackers often abuse popular web services to host malicious payloads. Since code-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 code-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 the GitHub service to deliver malicious payloads.

Prevalence in PyPI community

33 packages
found in
Top 100
206 packages
found in
Top 1k
1631 packages
found in
Top 10k
63840 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

Buffer overrun protection on Linux is achieved in two ways. The most common solution is to use the stack canary (also called cookie). The stack canary is a special value written onto the stack that allows the operating system to detect and terminate the program if a stack overrun occurs. In most cases, compilers will apply the stack canary conservatively in order to avoid a negative performance impact. Therefore, stack canaries are often used together with another stack overrun mitigation - fortified functions. Fortified functions are usually wrappers around standard glibc functions (such as memcpy) which perform boundary checks either at compile time or run time to determine if a memory violation has occurred. The compiler needs additional context to generate such calls (for example, array size that needs to be known at compile time). Because of this, the compiler will virtually never substitute all viable functions with their fortified counterparts in complex programs. However, when combined with the stack canary, fortified functions provide a good measure of buffer overrun protection.

Prevalence in PyPI community

13 packages
found in
Top 100
74 packages
found in
Top 1k
456 packages
found in
Top 10k
7612 packages
in community

Next steps

Presence of unfortified string functions may indicate use of unsafe programming practices, and you should avoid it if possible.
In GCC, enable fortified functions with -fstack-protector and -D_FORTIFY_SOURCE=2 flag, while using at least -O1 optimization level.

Problem

Buffer overrun protection on Linux is achieved in two ways. The most common solution is to use the stack canary (also called cookie). The stack canary is a special value written onto the stack that allows the operating system to detect and terminate the program if a stack overrun occurs. In most cases, compilers will apply the stack canary conservatively in order to avoid a negative performance impact. Therefore, stack canaries are often used together with another stack overrun mitigation - fortified functions. Fortified functions are usually wrappers around standard glibc functions (such as memcpy) which perform boundary checks either at compile time or run time to determine if a memory violation has occurred. The compiler needs additional context to generate such calls (for example, array size that needs to be known at compile time). Because of this, the compiler will virtually never substitute all viable functions with their fortified counterparts in complex programs. However, when combined with the stack canary, fortified functions provide a good measure of buffer overrun protection.

Prevalence in PyPI community

18 packages
found in
Top 100
112 packages
found in
Top 1k
646 packages
found in
Top 10k
13062 packages
in community

Next steps

Presence of unfortified memory functions may indicate use of unsafe programming practices, and you should avoid it if possible.
In GCC, enable fortified functions with -fstack-protector and -D_FORTIFY_SOURCE=2 flag, while using at least -O1 optimization level.

Problem

Buffer overrun protection on Linux is achieved in two ways. The most common solution is to use the stack canary (also called cookie). The stack canary is a special value written onto the stack that allows the operating system to detect and terminate the program if a stack overrun occurs. In most cases, compilers will apply the stack canary conservatively in order to avoid a negative performance impact. Therefore, stack canaries are often used together with another stack overrun mitigation - fortified functions. Fortified functions are usually wrappers around standard glibc functions (such as memcpy) which perform boundary checks either at compile time or run time to determine if a memory violation has occurred. The compiler needs additional context to generate such calls (for example, array size that needs to be known at compile time). Because of this, the compiler will virtually never substitute all viable functions with their fortified counterparts in complex programs. However, when combined with the stack canary, fortified functions provide a good measure of buffer overrun protection.

Prevalence in PyPI community

9 packages
found in
Top 100
58 packages
found in
Top 1k
349 packages
found in
Top 10k
8260 packages
in community

Next steps

Presence of some input functions may indicate use of unsafe programming practices, and you should avoid it if possible.
In GCC, enable fortified functions with -fstack-protector and -D_FORTIFY_SOURCE=2 flag, while using at least -O1 optimization level.

Top behaviors

Prevalence in PyPI community

Behavior often found in this community (Common)
13 packages
found in
Top 100
99 packages
found in
Top 1k
566 packages
found in
Top 10k
19246 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
9 packages
found in
Top 100
94 packages
found in
Top 1k
476 packages
found in
Top 10k
16117 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
8 packages
found in
Top 100
64 packages
found in
Top 1k
281 packages
found in
Top 10k
8972 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
9 packages
found in
Top 100
75 packages
found in
Top 1k
309 packages
found in
Top 10k
9292 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
11 packages
found in
Top 100
73 packages
found in
Top 1k
368 packages
found in
Top 10k
8619 packages
in community

Top vulnerabilities

No vulnerabilities found.