GHSA-3f63-hfp8-52jq BIT-pillow-2023-50447 CVE-2023-50447 PYSEC-2026-457
Arbitrary Code Execution in Pillow
High
PyPI
- Severity
- 0.0 / 10
- High · CVSS 4.0
- Exploit chance (EPSS)
- 1.70%
- Chance of exploitation in the next 30 days. Higher than 77.25% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-94 Improper Control of Generation of Code ('Code Injection')
- CWE-95 Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
- Fix
10.2.0- Upgrade pillow to this version or later
Description
Pillow through 10.1.0 allows PIL.ImageMath.eval Arbitrary Code Execution via the environment parameter, a different vulnerability than CVE-2022-22817 (which was about the expression parameter).
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| pillow | < 10.2.0 |
10.2.0 |
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.
pillow PyPI → 10.2.0
- pip
pip install --upgrade "pillow>=10.2.0"- poetry
poetry add "pillow>=10.2.0"- uv
uv add "pillow>=10.2.0"
Severity breakdown
CVSS 4.0 0.0
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
- Attack vector
- Network
- Attack complexity
- Low
- Attack requirements
- None
- Privileges required
- None
- User interaction
- None
- Confidentiality (vulnerable system)
- High
- Integrity (vulnerable system)
- High
- Availability (vulnerable system)
- High
- Confidentiality (subsequent systems)
- None
- Integrity (subsequent systems)
- None
- Availability (subsequent systems)
- None
CVSS 3.1 8.1
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
- Attack vector
- Network
- Attack complexity
- High
- Privileges required
- None
- 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].
CWE-95: Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes code syntax before using the input in a dynamic evaluation call (e.g. "eval").
How to prevent it
- Architecture and Design, Implementation: If possible, refactor your code so that it does not need to use eval() at all.
- 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.
- Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control. Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
- 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
Advisories
Packages
Other links
- github.com/python-pillow/Pillow/commit/45c726fd4daa63236a8f3653530f297dc87b160a
- devhub.checkmarx.com/cve-details/CVE-2023-50447
- duartecsantos.github.io/2023-01-02-CVE-2023-50447
- duartecsantos.github.io/2024-01-02-CVE-2023-50447
- github.com/python-pillow/Pillow/releases
- lists.debian.org/debian-lts-announce/2024/01/msg00019.html
- pillow.readthedocs.io/en/stable/releasenotes/10.2.0.html
- openwall.com/lists/oss-security/2024/01/20/1
- duartecsantos.github.io/2024-01-02-CVE-2023-50447
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| PYSEC-2023-175 Pillow versions before v10.0.1 bundled libwebp binaries in wheels that are vulnerable to CVE-2023-5129 (previously CVE-2023-4863). | High Known exploited | Also affects pillow |
| GHSA-j7hp-h8jx-5ppr libwebp: OOB write in BuildHuffmanTable | High Known exploited | Also affects pillow |
| GHSA-78g4-xqwv-q3g8 Pillow: Bundled libopenjp2 2.5.4 in all wheels since 11.1.0 carries OSV-2025-219 heap-buffer-overflow write reachable from Image.open().load() | High | Also affects pillow |
| GHSA-654x-cwwv-5j9c Pillow: Arrow export leaks stale heap memory when PILLOW_ALIGNMENT > 1 | Medium | Also affects pillow |
| GHSA-xvh5-g6gv-559v Pillow: Memory disclosure and segfault in libtiff encoder via crafted TIFF tags | High | Also affects pillow |
| GHSA-g7cr-3p24-7wf7 Pillow: Heap buffer overflow writing JPEG comment after ICC profile fills buffer | High | Also affects pillow |
| GHSA-9v9v-9qvf-h2hj Pillow: Heap over-read in WMF/EMF rendering via 16-bit dimension truncation (Windows builds) | High | Also affects pillow |
| GHSA-g4qv-2jxx-769h Pillow: PNG eXIf chunk makes EXIF parsing run in quadratic time | Medium | Also affects pillow |
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