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Axios: Excessive recursion in formDataToJSON can cause denial of service

Moderate severity GitHub Reviewed Published Jul 6, 2026 in axios/axios • Updated Jul 20, 2026

Package

npm axios (npm)

Affected versions

>= 0.28.0, < 0.33.0
>= 1.0.0, < 1.18.0

Patched versions

0.33.0
1.18.0

Description

Summary

Axios versions 0.28.0 and later contain uncontrolled recursion in formDataToJSON, the helper behind the public axios.formToJSON() / named formToJSON API and the default request transform used when FormData is sent with an application/json content type.

Applications are affected when they pass attacker-controlled FormData field names into this functionality. A field name with thousands of nested bracket segments can exhaust the JavaScript call stack and throw RangeError: Maximum call stack size exceeded, causing request failure and, in applications that do not handle the exception or rejected promise, possible process termination.

Impact

The impact is denial of service against applications that process untrusted FormData field names through axios' FormData-to-JSON conversion.

The vulnerable path is not reached by merely installing axios, by normal multipart FormData pass-through, or by ordinary axios requests that do not request JSON serialisation of FormData. In the default axios request, the error is produced before network I/O and returned as a rejected Promise. Direct use of formToJSON() throws synchronously.

Server-side applications are the primary risk when remote users can submit arbitrary form field names, and the application converts those fields with formToJSON() or sends them through axios as JSON.

Affected Functionality

Affected APIs and paths:

  • axios.formToJSON(formData)
  • import { formToJSON } from "axios"
  • lib/helpers/formDataToJSON.js
  • axios default transformRequest when data is FormData and Content-Type contains application/json

Unaffected or lower-risk paths:

  • Normal multipart FormData requests without JSON Content-Type
  • toFormData() object-to-FormData serialisation, which already has a maxDepth guard
  • Axios versions before 0.28.0, where this helper and public API were not present

Technical Details

lib/helpers/formDataToJSON.js parses a form field name into path segments with parsePropPath(). For a key such as a[x][x][x], each bracketed segment becomes another path element.

formDataToJSON() then calls the nested buildPath(path, value, target, index) function. buildPath() recursively calls itself once for each path segment and does not enforce a maximum depth:

const result = buildPath(path, value, target[name], index);

A key containing thousands of bracket segments, therefore, creates thousands of recursive calls. At sufficient depth, V8 throws RangeError: Maximum call stack size exceeded.

Axios already applies a depth guard to the inverse serializer in lib/helpers/toFormData.js, where maxDepth defaults to 100 and exceeding it throws AxiosError with code ERR_FORM_DATA_DEPTH_EXCEEDED. formDataToJSON() does not currently have equivalent protection.

Proof of Concept of Attack

import { formToJSON } from "axios";

const fd = new FormData();
fd.append("a" + "[x]".repeat(15000), "value");

try {
  formToJSON(fd);
  console.log("not vulnerable");
} catch (err) {
  console.log(`${err.constructor.name}: ${err.message}`);
}

Expected vulnerable result:

RangeError: Maximum call stack size exceeded

The axios request transform path can also be reached before network I/O:

import axios from "axios";

const fd = new FormData();
fd.append("a" + "[x]".repeat(15000), "value");

await axios
  .post("http://127.0.0.1:1/", fd, {
    headers: { "Content-Type": "application/json" }
  })
  .catch((err) => console.log(`${err.constructor.name}: ${err.message}`));

Expected vulnerable result:

RangeError: Maximum call stack size exceeded

Workarounds

Applications can avoid the vulnerable path by not converting attacker-controlled FormData to JSON with axios.

If conversion is required before a fixed axios release is available, validate FormData field names before calling formToJSON() or before sending FormData with Content-Type: application/json. Reject keys whose parsed nesting depth exceeds the application's expected schema.

For axios requests carrying untrusted FormData, avoid setting Content-Type: application/json; leaving the data as multipart FormData bypasses formDataToJSON().

Catching the resulting error can prevent process termination, but it does not remove the uncontrolled-recursion behaviour and should not be treated as the primary mitigation.

Original Report # Axios SSRF via Incomplete Loopback Detection ## CWE-918 | CVSS 7.5 (HIGH) | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L

1. Classification

CWE CVSS Score Severity Type
CWE-918 7.5 (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L) HIGH Server-Side Request Forgery

2. Description

Summary

The shouldBypassProxy() function in Axios fails to recognise 0.0.0.0, ::, and ::ffff:0.0.0.0 as loopback addresses. When NO_PROXY=localhost is configured, requests to these addresses are incorrectly forwarded through the proxy instead of being sent directly, enabling an SSRF attack against internal services reachable via the proxy's loopback interface.

Root Cause

File: lib/helpers/shouldBypassProxy.js

isIPv4Loopback (lines 3-8): Only checks for 127.x.x.x addresses by inspecting parts[0] !== '127'. The 0.0.0.0 address has parts[0] === '0', so it falls through as non-loopback, even though on Linux 0.0.0.0 routes to the loopback interface.

isIPv6Loopback (lines 10-38): Only checks host === '::1'. The :: address (unspecified IPv6) also routes to the loopback, but is not recognised.

Attack Flow:

isIPv4Loopback (line 3) — fails for 0.0.0.0
  → isLoopback (line 44) — wraps both checks, returns false
    → shouldBypassProxy (line 127) — PUBLIC API, exported default
      → lib/adapters/http.js (line 190) — Node.js HTTP adapter

Attack Vector

  • Access Vector: Network (AV:N)
  • Access Complexity: Low (AC:L) — attacker only needs control of a URL
  • Privileges Required: None (PR:N)
  • User Interaction: None (UI:N)

3. Proof of Concept

Phase 1: Logic Verification

import shouldBypassProxy from 'axios/lib/helpers/shouldBypassProxy.js';

// Normal loopback — correctly returns true (bypasses proxy)
shouldBypassProxy('http://127.0.0.1:9999/');  // → true

// Vulnerable — returns false (goes through proxy!)
shouldBypassProxy('http://0.0.0.0:9999/');    // → false  ← SSRF
shouldBypassProxy('http://[::]:9999/');        // → false  ← SSRF
shouldBypassProxy('http://[::ffff:0.0.0.0]:9999/'); // → false ← SSRF

Phase 2: Docker E2E Reproduction

A full 3-container Docker reproduction was created and tested:

  • Proxy container: Simple HTTP forward proxy on port 8888
  • Internal container: Internal service on port 9999 (simulates sensitive internal resource)
  • Attacker container: Runs the test script with Axios source mounted

Reproduction steps:

cd /tmp/deep-e2e
docker compose up -d
docker compose exec attacker node test-ssrf.js

Results:

  • Test 1: 127.0.0.1 + NO_PROXY=localhost → BYPASS (correct)
  • Test 2: 0.0.0.0 + NO_PROXY=localhost → VIA_PROXY (SSRF)
  • Test 3: [::] + NO_PROXY=localhost → VIA_PROXY (SSRF)
  • Test 4: [::ffff:0.0.0.0] + NO_PROXY=localhost → VIA_PROXY (SSRF)

Phase 3: Actual Axios Client

The real Axios HTTP client (v1.16.1, source tree) was tested through proxy configuration:

  • Axios with proxy: { host: 'proxy', port: 8888 }
  • Setting NO_PROXY=localhost and requesting http://0.0.0.0:9999/
  • Result: Axios forwarded the request through the proxy instead of bypassing it

4. Impact

Attack Scenario

  1. Attacker has control over a URL that an Axios client will request (direct input, redirect target, open redirect chain)
  2. The Axios client is configured with a proxy (e.g., corporate proxy) and NO_PROXY=localhost to protect internal services
  3. Attacker supplies http://0.0.0.0:8080/admin as the target URL
  4. Axios sends the request through the proxy
  5. The proxy resolves 0.0.0.0 → the proxy's own loopback → reaches the internal admin service on port 8080

Potential Consequences

  • Information disclosure (C:L): Internal service responses become accessible
  • Integrity impact (I:L): Attacker can trigger actions on internal services (if proxy supports PUT/POST/DELETE)
  • Availability impact (A:L): Limited — depends on internal service behavior

Likelihood

  • High — proxy bypass is a common pattern in microservice architectures
  • Medium — requires attacker control of a URL (not always available)

5. Remediation

Code Fix

File: lib/helpers/shouldBypassProxy.js

function isIPv4Loopback(host) {
  if (host === '0.0.0.0') return true;  // ADD THIS LINE
  const parts = host.split('.');
  if (parts.length !== 4) return false;
  if (parts[0] !== '127') return false;
  return parts.every(p => /^\d+$/.test(p) && Number(p) >= 0 && Number(p) <= 255);
}

function isIPv6Loopback(host) {
  if (host === '::1' || host === '::') return true;  // ADD '::'
  // ... rest of implementation
}

Workarounds

  • Add 0.0.0.0 and :: to the NO_PROXY environment variable explicitly
  • Use 127.0.0.1 instead of 0.0.0.0 in all internal service URLs
  • Implement URL validation to reject 0.0.0.0 and :: before passing to Axios

References

@jasonsaayman jasonsaayman published to axios/axios Jul 6, 2026
Published to the GitHub Advisory Database Jul 20, 2026
Reviewed Jul 20, 2026
Last updated Jul 20, 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 Present
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:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

EPSS score

Weaknesses

Uncontrolled Resource Consumption

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

Uncontrolled Recursion

The product does not properly control the amount of recursion that takes place, consuming excessive resources, such as allocated memory or the program stack. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-42h9-826w-cgv3

Source code

Credits

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