GHSA-659f-22xc-98f2

OpenClaw hook transform path containment missed symlink-resolved escapes

High npm

Severity: 0.0 / 10; High · CVSS 4.0
Exploit chance (EPSS): —; No score published
Known exploited: No; Not in CISA's catalog
Weakness: CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: 2026.2.22; Upgrade openclaw to this version or later

Description

Vulnerability

Webhook transform modules were validated with lexical path checks only. A symlink under the allowed hooks transform tree could resolve outside the intended directory and be dynamically imported.

Affected Packages / Versions

Package: openclaw (npm)
Affected versions: <= 2026.2.21-2
Patched version (planned next release): 2026.2.22

Impact

When an attacker can cause a transform module path to reference a symlinked entry that resolves outside the trusted transform directory, the gateway may import and execute unintended JavaScript with gateway-process privileges.

Attack Preconditions

Hook transforms are enabled and reachable.
Attacker can influence transform path resolution (for example via privileged config access and/or writable filesystem path in the transform tree).
A symlink escape exists to attacker-controlled code.

Remediation

Enforce realpath-aware containment for existing path ancestors before dynamic import.
Keep lexical containment checks for traversal and absolute-path escapes.
Add regression coverage for:
- transform module symlink escape rejection,
- hooks.transformsDir symlink escape rejection,
- in-root symlink allow-case.

Fix Commit(s)

f4dd0577b055f77af783105bd65eae32f3d5e6a1

OpenClaw thanks @aether-ai-agent for reporting.

Affected versions

npm

Package	Vulnerable	Fixed in
openclaw	`< 2026.2.22`	`2026.2.22`

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.

openclaw npm → `2026.2.22`

npm

npm install openclaw@2026.2.22

yarn

yarn add openclaw@2026.2.22

pnpm

pnpm add openclaw@2026.2.22

Severity breakdown

CVSS 4.0 0.0

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

Attack vector: Local
Attack complexity: High
Attack requirements: Present
Privileges required: Low
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

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

Packages

github.com/openclaw/openclaw

Timeline

Mar 3, 2026 Published
Mar 4, 2026 Last updated by the source

Advisory	Severity	Why it's related
GHSA-3x84-qq85-fj65 OpenClaw: Browser CDP discovery could accept blocked WebSocket URLs	High	Also affects openclaw
GHSA-9c3v-684m-579c OpenClaw MCP SSE redirects could forward Authorization headers	Medium	Also affects openclaw
GHSA-f397-5vjw-v2c2 OpenClaw: Shell inline-command parsing could miss an allowlist check	High	Also affects openclaw
GHSA-rx78-29qr-5hq8 OpenClaw: Workspace-derived service PATH could influence trash command selection	High	Also affects openclaw
GHSA-ccwh-wwpp-6wg5 OpenClaw: Host environment sanitizer missed two Node.js control variables	High	Also affects openclaw
GHSA-985f-72mj-8gf7 OpenClaw: Tool group policy callers could accept unvalidated group IDs	Medium	Also affects openclaw
GHSA-9v8j-9c9g-w66c OpenClaw: Bootstrap token replay could widen pending pairing scopes	Medium	Also affects openclaw
GHSA-c226-q6fx-6j6c OpenClaw: macOS Swift exec allowlist missed combined POSIX inline flags	Medium	Also affects openclaw

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