Spectra Assure
Community
warningRisk: Hardening
Scanned: 4 days ago

pyhmmer

Artifact:
latest
Top 10k
Cython bindings and Python interface to HMMER3.
License: Permissive (MIT)
Published: 15 days ago




SAFE Assessment

Compliance

Licenses
No license compliance issues
Secrets
No sensitive information found

Security

Vulnerabilities
No known vulnerabilities detected
Hardening
4 execution hijacking concerns

Threats

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

INCIDENTS FOR THIS VERSION:

Popularity

21.4M
Total Downloads
Contributor
Declared Dependencies
47
Dependents

Top issues

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

36 packages
found in
Top 100
219 packages
found in
Top 1k
1678 packages
found in
Top 10k
63.41k 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

14 packages
found in
Top 100
85 packages
found in
Top 1k
513 packages
found in
Top 10k
8.12k 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

24 packages
found in
Top 100
130 packages
found in
Top 1k
733 packages
found in
Top 10k
14.31k 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

10 packages
found in
Top 100
67 packages
found in
Top 1k
398 packages
found in
Top 10k
9.13k 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.

Problem

On Linux, external symbols are resolved via the procedure linkage table (PLT) and the global offset table (GOT). Without any protection, both are writable at runtime and thus leave the executable vulnerable to pointer hijacking - an attack where the function address is overwritten with an address of a malicious function. Pointer hijacking can be mitigated by using full read-only relocations, which instruct the compiler to unify global offset tables into a single read-only table. This requires that all external function symbols are resolved at load-time instead of during execution, and may increase loading time for large programs.

Prevalence in PyPI community

26 packages
found in
Top 100
135 packages
found in
Top 1k
847 packages
found in
Top 10k
17.09k packages
in community

Next steps

In most cases, it's recommended to use full read-only relocations (in GCC: -Wl,-z,relro,-z,now).
If the executable load-time is an issue, you should use partial read-only relocations.

Top behaviors

Prevalence in PyPI community

Behavior often found in this community (Common)
14 packages
found in
Top 100
98 packages
found in
Top 1k
585 packages
found in
Top 10k
18.68k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
10 packages
found in
Top 100
93 packages
found in
Top 1k
490 packages
found in
Top 10k
15.62k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
17 packages
found in
Top 100
126 packages
found in
Top 1k
796 packages
found in
Top 10k
25.62k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
36 packages
found in
Top 100
219 packages
found in
Top 1k
1678 packages
found in
Top 10k
63.42k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
34 packages
found in
Top 100
181 packages
found in
Top 1k
1324 packages
found in
Top 10k
54.83k packages
in community

Top vulnerabilities

No vulnerabilities found.