GHSA-hf26-vvmx-x8c8 CVE-2007-5741 PYSEC-2007-4

Plone Arbitrary Code Execution via Unsafe Handling of Pickles

Critical PyPI

Severity: 0.0 / 10; Critical · CVSS 4.0
Exploit chance (EPSS): 2.21%; Chance of exploitation in the next 30 days. Higher than 82.56% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: 2.5.5; Upgrade plone to this version or later

Description

Plone 2.5 through 2.5.4 and 3.0 through 3.0.2 allows remote attackers to execute arbitrary Python code via network data containing pickled objects for the (1) statusmessages or (2) linkintegrity module, which the module unpickles and executes.

Affected versions

PyPI

Package	Vulnerable	Fixed in
plone	`>= 2.5, < 2.5.5`	`2.5.5`
plone	`>= 3.0, < 3.0.3`	`3.0.3`
plone	`>= 2.5, < 2.5.5` `>= 3.0, < 3.0.3`	`2.5.5` `3.0.3`

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.

plone PyPI → `2.5.5`

pip

pip install --upgrade "plone>=2.5.5"

poetry

poetry add "plone>=2.5.5"

uv

uv add "plone>=2.5.5"

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 9.8

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

Attack vector: Network
Attack complexity: Low
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].

References

Advisories

Fixes and patches

Packages

github.com/plone/Plone

Timeline

Nov 7, 2007 Published
Jul 2, 2026 Last updated by the source
Oct 31, 2026 Latest EPSS score

Advisory	Severity	Why it's related
GHSA-xg5p-8wg5-rhxm Phone information disclosure vulnerability	High	Also affects plone
GHSA-5xfx-55x4-j223 Cross-Frame Scripting vulnerability has been found on Plone CMS	High	Also affects plone
GHSA-47p5-p3jw-w78w Server-Side Request Forgery in Plone CMS	High	Also affects plone
GHSA-cvwc-g7fw-7xrj Plone XSS Vulnerability	Medium	Also affects plone
GHSA-pwgm-jvqv-6v8p Plone anonymous access to sub-objects in CMFEditions where KwAsAttributes classes were publishable	High	Also affects plone
GHSA-46f9-f8jm-mw2x Plone Cross-site Scripting vulnerability in the LiveSearch module	Medium	Also affects plone
GHSA-hjp5-hv33-q58g Plone credentials stored in session cookie	Medium	Also affects plone
GHSA-593c-j348-f3gv Plone Improper Session Management	High	Also affects plone

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