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CAPEC-59: Session Credential Falsification through Prediction |
 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. Likelihood Of Attack Typical Severity Execution Flow Explore Find Session IDs: The attacker interacts with the target host and finds that session IDs are used to authenticate users. | Techniques |
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| An attacker makes many anonymous connections and records the session IDs assigned. | | An attacker makes authorized connections and records the session tokens or credentials issued. |
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. | Techniques |
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| Cryptanalysis. The attacker uses cryptanalysis to determine if the session IDs contain any cryptographic protections. | | Pattern tests. The attacker looks for patterns (odd/even, repetition, multiples, or other arithmetic relationships) between IDs | | Comparison against time. The attacker plots or compares the issued IDs to the time they were issued to check for correlation. |
Experiment Match issued IDs: The attacker brute forces different values of session ID and manages to predict a valid session ID. | Techniques |
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| The attacker models the session ID algorithm enough to produce a compatible session IDs, or just one match. |
Exploit Use matched Session ID: The attacker uses the falsified session ID to access the target system. | Techniques |
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| The attacker loads the session ID into their web browser and browses to restricted data or functionality. | | The attacker loads the session ID into their network communications and impersonates a legitimate user to gain access to data or functionality. |
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). |
Skills Required
[Level: Low] There are tools to brute force session ID. Those tools require a low level of knowledge. |
[Level: Medium] Predicting Session ID may require more computation work which uses advanced analysis such as statistical analysis. |
Consequences  This table specifies different individual consequences associated with the attack pattern. The Scope identifies the security property that is violated, while the Impact describes the negative technical impact that arises if an adversary succeeds in their attack. The Likelihood provides information about how likely the specific consequence is expected to be seen relative to the other consequences in the list. For example, there may be high likelihood that a pattern will be used to achieve a certain impact, but a low likelihood that it will be exploited to achieve a different impact.| Scope | Impact | Likelihood |
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Confidentiality Access Control Authorization | Gain Privileges | |
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. |
Example 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. See also: CVE-2006-6969 |
| 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. See also: CVE-2001-1534 |
Taxonomy Mappings CAPEC mappings to ATT&CK techniques leverage an inheritance model to streamline and minimize direct CAPEC/ATT&CK mappings. Inheritance of a mapping is indicated by text stating that the parent CAPEC has relevant ATT&CK mappings. Note that the ATT&CK Enterprise Framework does not use an inheritance model as part of the mapping to CAPEC.Relevant to the ATT&CK taxonomy mapping (see parent) Relevant to the WASC taxonomy mapping | Entry ID | Entry Name |
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| 18 | Credential/Session Prediction |
Relevant to the OWASP taxonomy mapping References
[REF-1] G. Hoglund and
G. McGraw. "Exploiting Software: How to Break Code". Addison-Wesley. 2004-02.
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 Content History | Submissions |
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| Submission Date | Submitter | Organization |
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| 2014-06-23 (Version 2.6) | CAPEC Content Team | The MITRE Corporation | | | Modifications |
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| Modification Date | Modifier | Organization |
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| 2017-08-04 (Version 2.11) | CAPEC Content Team | The MITRE Corporation | | Updated Related_Attack_Patterns | | 2020-07-30 (Version 3.3) | CAPEC Content Team | The MITRE Corporation | | Updated Execution_Flow | | 2020-12-17 (Version 3.4) | CAPEC Content Team | The MITRE Corporation | | Updated Taxonomy_Mappings | | 2021-06-24 (Version 3.5) | CAPEC Content Team | The MITRE Corporation | | Updated Related_Weaknesses |
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