GHSA-42h9-826w-cgv3

ADVISORY - github

Summary

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

Common Weakness Enumeration (CWE)

ADVISORY - github

Uncontrolled Resource Consumption

Uncontrolled Recursion


GitHub

CREATED

UPDATED

EXPLOITABILITY SCORE

-

EXPLOITS FOUND
-
COMMON WEAKNESS ENUMERATION (CWE)

CVSS SCORE

6.3medium
PackageTypeOS NameOS VersionAffected RangesFix Versions
axiosnpm-->=1.0.0,<1.18.01.18.0
axiosnpm-->=0.28.0,<0.33.00.33.0

CVSS:4 Severity and metrics

The CVSS metrics represent different qualitative aspects of a vulnerability that impact the overall score, as defined by the CVSS Specification.

The vulnerable component is bound to the network stack, but the attack is limited at the protocol level to a logically adjacent topology. This can mean an attack must be launched from the same shared physical (e.g., Bluetooth or IEEE 802.11) or logical (e.g., local IP subnet) network, or from within a secure or otherwise limited administrative domain (e.g., MPLS, secure VPN to an administrative network zone). One example of an Adjacent attack would be an ARP (IPv4) or neighbor discovery (IPv6) flood leading to a denial of service on the local LAN segment (e.g., CVE-2013-6014).

Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success when attacking the vulnerable component.

The successful attack depends on the presence of specific deployment and execution conditions of the vulnerable system that enable the attack. These include: A race condition must be won to successfully exploit the vulnerability. The successfulness of the attack is conditioned on execution conditions that are not under full control of the attacker. The attack may need to be launched multiple times against a single target before being successful. Network injection. The attacker must inject themselves into the logical network path between the target and the resource requested by the victim (e.g. vulnerabilities requiring an on-path attacker).

The attacker is unauthenticated prior to attack, and therefore does not require any access to settings or files of the vulnerable system to carry out an attack.

The vulnerable system can be exploited without interaction from any human user, other than the attacker. Examples include: a remote attacker is able to send packets to a target system a locally authenticated attacker executes code to elevate privileges.

There is no loss of confidentiality within the Vulnerable System.

There is no loss of confidentiality within the Subsequent System or all confidentiality impact is constrained to the Vulnerable System.

There is no loss of integrity within the Vulnerable System.

There is no loss of integrity within the Subsequent System or all integrity impact is constrained to the Vulnerable System.

Performance is reduced or there are interruptions in resource availability. Even if repeated exploitation of the vulnerability is possible, the attacker does not have the ability to completely deny service to legitimate users. The resources in the Vulnerable System are either partially available all of the time, or fully available only some of the time, but overall there is no direct, serious consequence to the Vulnerable System.

There is no impact to availability within the Subsequent System or all availability impact is constrained to the Vulnerable System.