CVE-2026-35640: OpenClaw unauthenticated webhook

Q: Is CVE-2026-35640 actively exploited?

No confirmed active exploitation of CVE-2026-35640 has been reported, but organizations should still patch proactively.

Q: How to fix CVE-2026-35640?

1. Upgrade openclaw to version 2026.3.25 or later (fix commit: 5e8cb22176e9235e224be0bc530699261eb60e53 reorders logic to validate signature before parsing). 2. As a temporary workaround pre-patch, enforce aggressive rate limiting on Feishu webhook endpoints at the reverse proxy, load balancer, or WAF layer. 3. Restrict webhook endpoint exposure to Feishu's published source IP ranges only — do not expose to open internet. 4. Given the package's 67-CVE history, conduct a dependency risk review: evaluate whether openclaw is a viable long-term dependency for production AI agent workloads or warrants replacement.

Q: What systems are affected by CVE-2026-35640?

This vulnerability affects the following AI/ML architecture patterns: agent frameworks, webhook integrations, event-driven AI pipelines.

Q: What is the CVSS score for CVE-2026-35640?

CVE-2026-35640 has a CVSS v3.1 base score of 5.3 (MEDIUM). The EPSS exploitation probability is 0.44%.

CISO Take

The openclaw npm package processed incoming Feishu webhook request bodies in full before validating request signatures, meaning any unauthenticated attacker could trigger expensive JSON parsing operations at will. The barrier to exploitation is trivially low — a simple flood of crafted POST requests to the webhook endpoint is sufficient, no credentials or AI/ML knowledge required. No public exploit code exists and this is not in CISA KEV, but the package's broader security record of 67 CVEs signals systemic hygiene problems that elevate organizational risk beyond this single finding. Upgrade to openclaw 2026.3.25 or later immediately, and rate-limit webhook endpoints at the reverse proxy or WAF layer as defense-in-depth while patching.

Sources: NVD GitHub Advisory ATLAS

What is the risk?

Medium risk in isolation, elevated in organizational context. The vulnerability requires no credentials and no specialized knowledge — any attacker with network access to the webhook endpoint can trigger resource consumption. No public exploit code, no active exploitation observed, and no EPSS data available. The package's track record of 67 CVEs against the same codebase indicates structural security debt that organizations relying on openclaw for production AI agent workflows should treat as a systemic supplier risk, not just an isolated patch event.

How does the attack unfold?

Target Discovery

Adversary identifies a publicly accessible openclaw Feishu webhook endpoint through network scanning or reconnaissance of the target organization's AI agent infrastructure.

AML.T0006

Webhook Exploitation

Adversary floods the unauthenticated webhook endpoint with POST requests containing large or deeply nested JSON bodies, triggering full parsing on every request before signature rejection.

AML.T0049

Resource Exhaustion

Server parsing threads saturate under sustained request load; CPU and memory consumption spike as each unauthenticated payload is fully deserialized before being discarded.

AML.T0034.000

AI Agent Disruption

Legitimate Feishu events go unprocessed as the agent becomes unresponsive, halting automated workflows and cascading failures to any downstream agents or pipelines awaiting Feishu-sourced triggers.

AML.T0029

Target Discovery

Adversary identifies a publicly accessible openclaw Feishu webhook endpoint through network scanning or reconnaissance of the target organization's AI agent infrastructure.

AML.T0006

Webhook Exploitation

Adversary floods the unauthenticated webhook endpoint with POST requests containing large or deeply nested JSON bodies, triggering full parsing on every request before signature rejection.

AML.T0049

Resource Exhaustion

Server parsing threads saturate under sustained request load; CPU and memory consumption spike as each unauthenticated payload is fully deserialized before being discarded.

AML.T0034.000

AI Agent Disruption

Legitimate Feishu events go unprocessed as the agent becomes unresponsive, halting automated workflows and cascading failures to any downstream agents or pipelines awaiting Feishu-sourced triggers.

AML.T0029

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
OpenClaw	npm	< 2026.3.28	`2026.3.28`
4 dependents 37% patched ~3d to patch Full package profile →

Do you use OpenClaw? You're affected.

How severe is it?

CVSS 3.1

5.3 / 10

EPSS

0.4%

chance of exploitation in 30 days

Higher than 35% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Trivial

What is the attack surface?

AV Network

AC Low

PR None

UI None

S Unchanged

C None

I None

A Low

What should I do?

4 steps

Upgrade openclaw to version 2026.3.25 or later (fix commit: 5e8cb22176e9235e224be0bc530699261eb60e53 reorders logic to validate signature before parsing).
As a temporary workaround pre-patch, enforce aggressive rate limiting on Feishu webhook endpoints at the reverse proxy, load balancer, or WAF layer.
Restrict webhook endpoint exposure to Feishu's published source IP ranges only — do not expose to open internet.
Given the package's 67-CVE history, conduct a dependency risk review: evaluate whether openclaw is a viable long-term dependency for production AI agent workloads or warrants replacement.

What does CISA's SSVC say?

Decision Track

Exploitation none

Automatable Yes

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

How is it classified?

DoS Agent AML.T0029 - Denial of AI Service AML.T0034.000 - Excessive Queries AML.T0049 - Exploit Public-Facing Application

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Art. 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2.3 - Availability of AI systems

NIST AI RMF

MANAGE-2.2 - AI Risk Treatment and Monitoring

OWASP LLM Top 10

LLM04 - Model Denial of Service

Frequently Asked Questions

What is CVE-2026-35640?

The openclaw npm package processed incoming Feishu webhook request bodies in full before validating request signatures, meaning any unauthenticated attacker could trigger expensive JSON parsing operations at will. The barrier to exploitation is trivially low — a simple flood of crafted POST requests to the webhook endpoint is sufficient, no credentials or AI/ML knowledge required. No public exploit code exists and this is not in CISA KEV, but the package's broader security record of 67 CVEs signals systemic hygiene problems that elevate organizational risk beyond this single finding. Upgrade to openclaw 2026.3.25 or later immediately, and rate-limit webhook endpoints at the reverse proxy or WAF layer as defense-in-depth while patching.

Is CVE-2026-35640 actively exploited?

No confirmed active exploitation of CVE-2026-35640 has been reported, but organizations should still patch proactively.

How to fix CVE-2026-35640?

1. Upgrade openclaw to version 2026.3.25 or later (fix commit: 5e8cb22176e9235e224be0bc530699261eb60e53 reorders logic to validate signature before parsing). 2. As a temporary workaround pre-patch, enforce aggressive rate limiting on Feishu webhook endpoints at the reverse proxy, load balancer, or WAF layer. 3. Restrict webhook endpoint exposure to Feishu's published source IP ranges only — do not expose to open internet. 4. Given the package's 67-CVE history, conduct a dependency risk review: evaluate whether openclaw is a viable long-term dependency for production AI agent workloads or warrants replacement.

What systems are affected by CVE-2026-35640?

This vulnerability affects the following AI/ML architecture patterns: agent frameworks, webhook integrations, event-driven AI pipelines.

What is the CVSS score for CVE-2026-35640?

CVE-2026-35640 has a CVSS v3.1 base score of 5.3 (MEDIUM). The EPSS exploitation probability is 0.44%.

What is the AI security impact?

Affected AI Architectures

agent frameworkswebhook integrationsevent-driven AI pipelines

MITRE ATLAS Techniques

AML.T0029 Denial of AI Service

AML.T0034.000 Excessive Queries

AML.T0049 Exploit Public-Facing Application

Compliance Controls Affected

EU AI Act: Art. 15

ISO 42001: A.6.2.3

NIST AI RMF: MANAGE-2.2

OWASP LLM Top 10: LLM04

What are the technical details?

Original Advisory

## Summary Feishu webhook reads and parses unauthenticated request bodies before signature validation ## Affected Packages / Versions - Package: `openclaw` - Affected versions: `<= 2026.3.24` - First patched version: `2026.3.25` - Latest published npm version at verification time: `2026.3.24` ## Details Feishu webhook handling previously parsed JSON before signature validation, which let unauthenticated callers force full JSON parsing work before rejection. Commit `5e8cb22176e9235e224be0bc530699261eb60e53` reads the raw request body, validates the signature first, and only then parses JSON. Verified vulnerable on tag `v2026.3.24` and fixed on `main` by commit `5e8cb22176e9235e224be0bc530699261eb60e53`. ## Fix Commit(s) - `5e8cb22176e9235e224be0bc530699261eb60e53`

Exploitation Scenario

An adversary identifies that a target organization's openclaw-powered AI agent exposes a Feishu webhook endpoint publicly. Without any credentials, they run a script that floods the endpoint with POST requests containing deeply nested or maximally large JSON payloads. Each request triggers full JSON deserialization on the server before the signature check rejects it. Under sustained load, the server's parsing threads saturate, causing the AI agent to become unresponsive to legitimate Feishu events — effectively blinding the agent to incoming triggers and halting any downstream automated workflows, tool invocations, or multi-agent coordination that depends on Feishu as a signal source.

Weaknesses (CWE)

CWE-400 Uncontrolled Resource Consumption Primary CWE-696 Incorrect Behavior Order

CWE-400 — Uncontrolled Resource Consumption: The product does not properly control the allocation and maintenance of a limited resource.

[Architecture and Design] Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
[Architecture and Design] Mitigation of resource exhaustion attacks requires that the target system either: The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution is simply difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply makes the attack require more resources on the part of the attacker. recognizes the attack and denies that user further access for a given amount of time, or uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed.

Source: MITRE CWE corpus.