Predictable Exact Value from Previous Values

Opis

An exact value or random number can be precisely predicted by observing previous values.

Skutki

• Other

  Varies by Context

Zapobieganie

• Increase the entropy used to seed a PRNG.

• Architecture and Design, Requirements · Libraries or Frameworks

  Use products or modules that conform to FIPS 140-2 [REF-267] to avoid obvious entropy problems. Consult FIPS 140-2 Annex C ("Approved Random Number Generators").

• 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.

Przykłady podatności

• CVE-2002-1463 Firewall generates easily predictable initial sequence numbers (ISN), which allows remote attackers to spoof connections.
• CVE-1999-0074 Listening TCP ports are sequentially allocated, allowing spoofing attacks.
• CVE-1999-0077 Predictable TCP sequence numbers allow spoofing.
• CVE-2000-0335 DNS resolver uses predictable IDs, allowing a local user to spoof DNS query results.

Słabość nadrzędna

• CWE-340 Generation of Predictable Numbers or Identifiers

Kiedy powstaje

• Architecture and Design
• Implementation

Dotyczy

Not Language-Specific

Uwagi

As of CWE 4.5, terminology related to randomness, entropy, and predictability can vary widely. Within the developer and other communities, "randomness" is used heavily. However, within cryptography, "entropy" is distinct, typically implied as a measurement. There are no commonly-used definitions, even within standards documents and cryptography papers. Future versions of CWE will attempt to define these terms and, if necessary, distinguish between them in ways that are appropriate for different communities but do not reduce the usability of CWE for mapping, understanding, or other scenarios.

Źródła

1. FIPS PUB 140-2: SECURITY REQUIREMENTS FOR CRYPTOGRAPHIC MODULES — Information Technology Laboratory, National Institute of Standards and Technology, 2001
2. 24 Deadly Sins of Software Security — Michael Howard, David LeBlanc, John Viega, "Sin 20: Weak Random Numbers." Page 299, McGraw-Hill, 2010