GHSA-72m8-9m7m-h278 CVE-2026-59821 PYSEC-2026-3478

LiteLLM: Custom Code Guardrails production endpoints bypass code safety checks

High PyPI

Severity: 0.0 / 10; High · CVSS 4.0
Exploit chance (EPSS): 0.90%; Chance of exploitation in the next 30 days. Higher than 59.80% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: 1.82.0; Upgrade litellm to this version or later

Description

Impact

LiteLLM's Custom Code Guardrails production create/update paths did not apply the same sandboxing and validation used by the test endpoint.

A privileged user with access to create or update guardrails could submit custom Python code that executed in the LiteLLM proxy environment. In deployments without a configured master key, callers could be treated as proxy administrators, making this reachable without intended administrative authorization.

This could allow arbitrary code execution in the LiteLLM proxy container and exposure of secrets available to the process.

Patches

The issue is fixed in 1.82.0-stable.

LiteLLM recommend upgrading to 1.82.0-stable or later.

Workarounds

If upgrading is not immediately possible:

Restrict access to POST /guardrails and PUT /guardrails/{guardrail_id} to trusted administrators only.
Ensure LITELLM_MASTER_KEY is configured.
Avoid enabling Custom Code Guardrails for untrusted users.

Affected versions

PyPI

Package	Vulnerable	Fixed in
litellm	`< 1.82.0`	`1.82.0`
litellm	`< 1.82.0`	`1.82.0`

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.

litellm PyPI → `1.82.0`

pip

pip install --upgrade "litellm>=1.82.0"

poetry

poetry add "litellm>=1.82.0"

uv

uv add "litellm>=1.82.0"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:N/AC:L/AT:P/PR:H/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N

Attack vector: Network
Attack complexity: Low
Attack requirements: Present
Privileges required: High
User interaction: None
Confidentiality (vulnerable system): Low
Integrity (vulnerable system): Low
Availability (vulnerable system): Low
Confidentiality (subsequent systems): None
Integrity (subsequent systems): None
Availability (subsequent systems): None

CVSS 3.1 7.2

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

Attack vector: Network
Attack complexity: Low
Privileges required: High
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

Packages

Timeline

Jul 8, 2026 Published
Oct 25, 2026 Last updated by the source
Oct 31, 2026 Latest EPSS score

Advisory	Severity	Why it's related
GHSA-7488-6r32-c95q LiteLLM: MCP Authentication Bypass via OAuth2 Passthrough Fallback	High Known exploited	Also affects litellm
GHSA-v4p8-mg3p-g94g LiteLLM: Authenticated command execution via MCP stdio test endpoints	High Known exploited	Also affects litellm
GHSA-r75f-5x8p-qvmc LiteLLM has SQL Injection in Proxy API key verification	Critical Known exploited	Also affects litellm
GHSA-g5ff-637f-6q2m LiteLLM: internal_user_viewer arbitrary local file read via `vertex_ai_credentials`	High	Also affects litellm
GHSA-hhww-mrg2-969h LiteLLM vulnerable to reflected XSS in /sso/debug/callback	Medium	Also affects litellm
GHSA-7hp6-4w63-5g45 LiteLLM: Privilege Escalation to Proxy Admin via Cross-Domain Reuse of the Salt Key	Critical	Also affects litellm
GHSA-hx8v-g79f-8w5f LiteLLM Proxy has server-side request forgery via the `user_config` request parameter	Medium	Also affects litellm
GHSA-3cv6-jpf6-8222 LiteLLM: Authenticated SSRF and provider-credential exfiltration via unvalidated request-body routing parameters	Medium	Also affects litellm

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