CWE-337 Variant Draft

Predictable Seed in Pseudo-Random Number Generator (PRNG)

This vulnerability occurs when a Pseudo-Random Number Generator (PRNG) uses an easily guessable starting value, like the current system time or a process ID, to begin its sequence.

Definition

What is CWE-337?

This vulnerability occurs when a Pseudo-Random Number Generator (PRNG) uses an easily guessable starting value, like the current system time or a process ID, to begin its sequence.
A PRNG's security depends heavily on the secrecy and unpredictability of its initial seed. When developers use common, low-entropy sources—such as timestamps, process IDs, or other publicly available system values—they dramatically shrink the pool of possible starting points. An attacker can often deduce or narrow down these seeds with minimal effort, compromising the entire random sequence that follows. In practice, this means that cryptographic operations, session tokens, random identifiers, or any security mechanism relying on this PRNG become predictable. Instead of facing a vast, unguessable number of possibilities, an attacker can run a small, feasible set of seed guesses to replicate the generator's output, bypassing protections that were assumed to be random.
Real-world impact

Real-world CVEs caused by CWE-337

  • Cloud application on Kubernetes generates passwords using a weak random number generator based on deployment time.

  • server uses erlang:now() to seed the PRNG, which results in a small search space for potential random seeds

  • The removal of a couple lines of code caused Debian's OpenSSL Package to only use the current process ID for seeding a PRNG

  • Router's PIN generation is based on rand(time(0)) seeding.

  • cloud provider product uses a non-cryptographically secure PRNG and seeds it with the current time

How attackers exploit it

Step-by-step attacker path

  1. 1

    Identify a code path that handles untrusted input without validation.

  2. 2

    Craft a payload that exercises the unsafe behavior — injection, traversal, overflow, or logic abuse.

  3. 3

    Deliver the payload through a normal request and observe the application's reaction.

  4. 4

    Iterate until the response leaks data, executes attacker code, or escalates privileges.

Vulnerable code example

Vulnerable Java

Both of these examples use a statistical PRNG seeded with the current value of the system clock to generate a random number:

Vulnerable Java
Random random = new Random(System.currentTimeMillis());
  int accountID = random.nextInt();
Secure code example

Secure pseudo

Secure pseudo
// Validate, sanitize, or use a safe API before reaching the sink.
function handleRequest(input) {
  const safe = validateAndEscape(input);
  return executeWithGuards(safe);
}
What changed: the unsafe sink is replaced (or the input is validated/escaped) so the same payload no longer triggers the weakness.
Prevention checklist

How to prevent CWE-337

  • Use non-predictable inputs for seed generation.
  • Architecture and Design / Requirements Use products or modules that conform to FIPS 140-2 [REF-267] to avoid obvious entropy problems, or use the more recent FIPS 140-3 [REF-1192] if possible.
  • Implementation Use a PRNG that periodically re-seeds itself using input from high-quality sources, such as hardware devices with high entropy. However, do not re-seed too frequently, or else the entropy source might block.
Detection signals

How to detect CWE-337

SAST High

Run static analysis (SAST) on the codebase looking for the unsafe pattern in the data flow.

DAST Moderate

Run dynamic application security testing against the live endpoint.

Runtime Moderate

Watch runtime logs for unusual exception traces, malformed input, or authorization bypass attempts.

Code review Moderate

Code review: flag any new code that handles input from this surface without using the validated framework helpers.

CWE-337

Don't catalog this weakness. Prove it's reachable.

Plexicus turns CWE catalogs into evidence: every CWE-pattern is matched against your real code graph, reach is proven on a sandbox clone, and verified findings ship as reviewed PRs.

Frequently asked questions

Frequently asked questions

What is CWE-337?

This vulnerability occurs when a Pseudo-Random Number Generator (PRNG) uses an easily guessable starting value, like the current system time or a process ID, to begin its sequence.

How serious is CWE-337?

MITRE has not published a likelihood-of-exploit rating for this weakness. Treat it as medium-impact until your threat model proves otherwise.

What languages or platforms are affected by CWE-337?

MITRE has not specified affected platforms for this CWE — it can apply across most application stacks.

How can I prevent CWE-337?

Use non-predictable inputs for seed generation. Use products or modules that conform to FIPS 140-2 [REF-267] to avoid obvious entropy problems, or use the more recent FIPS 140-3 [REF-1192] if possible.

How does Plexicus detect and fix CWE-337?

Plexicus's SAST engine matches the data-flow signature for CWE-337 on every commit. When a match is found, our Codex Remedium agent opens a fix PR with the corrected code, tests, and a one-line summary for the reviewer.

Where can I learn more about CWE-337?

MITRE publishes the canonical definition at https://cwe.mitre.org/data/definitions/337.html. You can also reference OWASP and NIST documentation for adjacent guidance.

Ready to validate what matters?

Ready to validate what matters?

Plexicus is Proof-Driven AppSec: validated findings, contextual understanding, and reviewed remediation — anchored in evidence, scoped with you.

Qualification

Check whether AI Swarm Pentest fits your environment.

Share the minimum context. We will review the scope and tell you the next commercial step.

Before submitting — verify you fit

Teams with fewer than 50 developers: start a 14-day Trial instead of booking a demo. Start a 14-day Trial →

0 / 280

No commitment. If you don't fit, we'll tell you.

SAMPLE HANDOVER · ILLUSTRATIVE

Sample evidence handover

A trimmed view of what your team receives at the end of an AI Swarm Pentest engagement. Real engagements include full technical evidence, executive narrative, and a remediation plan.

VALIDATED FINDING Evidence attached

Server-Side Request Forgery in webhooks/receiver

demo-project/sample-app · src/webhooks/receiver.py:42

SeverityHigh CVSS 3.18.6 Priority79 Confirmedvia replay

Untrusted caller-supplied URLs reach an internal egress without an allowlist. Replayed in a sandbox against a fresh authorised target — the same control was validated to fail twice.

REVIEWER-READY REMEDIATION Merge-ready PR

Validate the target URL against an allowlist of permitted hostnames. Reject private/internal IP ranges. Enforce HTTPS only.

plexicus/remediation/webhooks-ssrf 3 changed · 0 new files
42resp = requests.get(target_url)
42+if not is_allowed_host(target_url):
43+  raise WebhookRejected(target_url)
44+resp = requests.get(target_url, timeout=5)
Every engagement hands over:
  • Executive briefing
  • Validated findings list
  • Merge-ready PRs
  • Compliance mapping (NIS2 · DORA · CRA)