Session Credential Falsification through Prediction
Attack Pattern ID: 59 (Detailed Attack Pattern Completeness: Complete)Typical Severity: HighStatus: Draft
+ Description


This attack targets predictable session ID in order to gain privileges. The attacker can predict the session ID used during a transaction to perform spoofing and session hijacking.

Attack Execution Flow

  1. Find Session IDs:

    The attacker interacts with the target host and finds that session IDs are used to authenticate users.

    Attack Step Techniques

    IDAttack Step Technique DescriptionEnvironments

    An attacker makes many anonymous connections and records the session IDs assigned.

    env-Web env-Peer2Peer env-CommProtocol env-ClientServer

    An attacker makes authorized connections and records the session tokens or credentials issued.

    env-Web env-Peer2Peer env-CommProtocol env-ClientServer


    IDtypeIndicator DescriptionEnvironments

    Web applications use session IDs


    Network systems issue session IDs or connection IDs

    env-CommProtocol env-ClientServer env-Peer2Peer

    Security Controls

    IDtypeSecurity Control Description
    Monitor logs for unusual amounts of invalid sessions.
    Monitor logs for unusual amounts of invalid connections or invalid requests from unauthorized hosts.
  2. Characterize IDs:

    The attacker studies the characteristics of the session ID (size, format, etc.). As a results the attacker finds that legitimate session IDs are predictable.

    Attack Step Techniques

    IDAttack Step Technique DescriptionEnvironments

    Cryptanalysis. The attacker uses cryptanalysis to determine if the session IDs contain any cryptographic protections.

    env-Web env-ClientServer env-Peer2Peer env-CommProtocol

    Pattern tests. The attacker looks for patterns (odd/even, repetition, multiples, or other arithmetic relationships) between IDs

    env-Web env-ClientServer env-Peer2Peer env-CommProtocol

    Comparison against time. The attacker plots or compares the issued IDs to the time they were issued to check for correlation.

    env-Web env-ClientServer env-Peer2Peer env-CommProtocol


    IDtypeOutcome Description
    Patterns are detectable in session IDs
    Session IDs pass NIST FIPS 140 statistical tests for cryptographic randomness.
    Session IDs are repeated.
  1. Match issued IDs:

    The attacker brute forces different values of session ID and manages to predict a valid session ID.

    Attack Step Techniques

    IDAttack Step Technique DescriptionEnvironments

    The attacker models the session ID algorithm enough to produce a compatible series os IDs, or just one match.

    env-Web env-ClientServer env-Peer2Peer env-CommProtocol


    IDtypeOutcome Description
    Session identifiers successfully spoofed
    No session IDs can be found or exploited
  1. Use matched Session ID:

    The attacker uses the falsified session ID to access the target system.

    Attack Step Techniques

    IDAttack Step Technique DescriptionEnvironments

    The attacker loads the session ID into his web browser and browses to restricted data or functionality.


    The attacker loads the session ID into his network communications and impersonates a legitimate user to gain access to data or functionality.

    env-CommProtocol env-Peer2Peer env-ClientServer

    Security Controls

    IDtypeSecurity Control Description
    Monitor the correlation between session IDs and other station designations (MAC address, IP address, VLAN, etc.). Alert on session ID reuse from multiple sources.
    Terminate both sessions if an ID is used from multiple origins.
+ Attack Prerequisites

The target host uses session IDs to keep track of the users.

Session IDs are used to control access to resources.

The session IDs used by the target host are predictable.For example, the session IDs are generated using predictable information (e.g., time).

+ Typical Likelihood of Exploit

Likelihood: High

+ Methods of Attack
  • Spoofing
  • Brute Force
  • Analysis
+ Examples-Instances


Jetty before 4.2.27, 5.1 before 5.1.12, 6.0 before 6.0.2, and 6.1 before 6.1.0pre3 generates predictable session identifiers using java.util.random, which makes it easier for remote attackers to guess a session identifier through brute force attacks, bypass authentication requirements, and possibly conduct cross-site request forgery attacks.

Related Vulnerabilities



mod_usertrack in Apache 1.3.11 through 1.3.20 generates session ID's using predictable information including host IP address, system time and server process ID, which allows local users to obtain session ID's and bypass authentication when these session ID's are used for authentication.

Related Vulnerabilities


+ Attacker Skills or Knowledge Required

Skill or Knowledge Level: Low

There are tools to brute force sesion ID. Those tools require a low level of knowledge.

Skill or Knowledge Level: Medium

Predicting Session ID may require more computation work which uses advanced analysis such as statistic analysis.

+ Probing Techniques

The attacker can perform analysis of the randomness of the session generation algortihm.

The attacker may need to steal a few valid session IDs using a different type of attack. And then use those session ID to predict the following ones.

The attacker can use brute force tools to find a valid session ID.

+ Solutions and Mitigations

Use a strong source of randomness to generate a session ID.

Use adequate length session IDs

Do not use information available to the user in order to generate session ID (e.g., time).

Ideas for creating random numbers are offered by Eastlake [RFC1750]

Encrypt the session ID if you expose it to the user. For instance session ID can be stored in a cookie in encrypted format.

+ Attack Motivation-Consequences
  • Privilege Escalation
+ Related Weaknesses
CWE-IDWeakness NameWeakness Relationship Type
290Authentication Bypass by SpoofingTargeted
330Use of Insufficiently Random ValuesTargeted
331Insufficient EntropyTargeted
346Origin Validation ErrorTargeted
488Data Leak Between SessionsSecondary
539Information Leak Through Persistent CookiesSecondary
200Information ExposureSecondary
6J2EE Misconfiguration: Insufficient Session-ID LengthTargeted
285Improper Access Control (Authorization)Secondary
384Session FixationSecondary
693Protection Mechanism FailureTargeted
719OWASP Top Ten 2007 Category A8 - Insecure Cryptographic StorageSecondary
+ Related Attack Patterns
NatureTypeIDNameDescriptionView(s) this relationship pertains toView\(s\)
ChildOfAttack PatternAttack Pattern196Session Credential Falsification through Forging 
Mechanism of Attack (primary)1000
ChildOfCategoryCategory351WASC Threat Classification 2.0 - WASC-18 - Credential/Session Prediction 
WASC Threat Classification 2.0333
+ Related Security Principles
  • Securing the Weakest Link

+ Purposes
  • Penetration
+ CIA Impact
Confidentiality Impact: HighIntegrity Impact: HighAvailability Impact: Low
+ Technical Context
Architectural Paradigms
+ References
G. Hoglund and G. McGraw. "Exploiting Software: How to Break Code". Addison-Wesley. February 2004.
+ Content History
Eric DalciCigital, Inc2007-01-25
Sean BarnumCigital, Inc2007-03-07Review and revise