The web application does not sufficiently verify inputs that are assumed to be immutable but are actually externally controllable, such as hidden form fields.

If a web product does not properly protect assumed-immutable values from modification in hidden form fields, parameters, cookies, or URLs, this can lead to modification of critical data. Web applications often mistakenly make the assumption that data passed to the client in hidden fields or cookies is not susceptible to tampering. Improper validation of data that are user-controllable can lead to the application processing incorrect, and often malicious, input.

For example, custom cookies commonly store session data or persistent data across sessions. This kind of session data is normally involved in security related decisions on the server side, such as user authentication and access control. Thus, the cookies might contain sensitive data such as user credentials and privileges. This is a dangerous practice, as it can often lead to improper reliance on the value of the client-provided cookie by the server side application.

Modes Of Introduction

Implementation : OMISSION: This weakness is caused by missing a security tactic during the architecture and design phase.

Applicable Platforms

Language

Class: Not Language-Specific (Undetermined)

Common Consequences

Observed Examples

Potential Mitigations

Phases : Implementation

Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.

When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue."

Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.

Phases : Implementation
Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180). Make sure that the application does not decode the same input twice (CWE-174). Such errors could be used to bypass allowlist validation schemes by introducing dangerous inputs after they have been checked.

Detection Methods

Automated Static Analysis

Automated static analysis, commonly referred to as Static Application Security Testing (SAST), can find some instances of this weakness by analyzing source code (or binary/compiled code) without having to execute it. Typically, this is done by building a model of data flow and control flow, then searching for potentially-vulnerable patterns that connect "sources" (origins of input) with "sinks" (destinations where the data interacts with external components, a lower layer such as the OS, etc.)
Effectiveness : High

Vulnerability Mapping Notes

Rationale : This CWE entry is at the Base level of abstraction, which is a preferred level of abstraction for mapping to the root causes of vulnerabilities.
Comments : Carefully read both the name and description to ensure that this mapping is an appropriate fit. Do not try to 'force' a mapping to a lower-level Base/Variant simply to comply with this preferred level of abstraction.

Related Attack Patterns

Notes

This is a primary weakness for many other weaknesses and functional consequences, including XSS, SQL injection, path disclosure, and file inclusion.
This is a technology-specific MAID problem.

References

REF-44

24 Deadly Sins of Software Security
Michael Howard, David LeBlanc, John Viega.

REF-62

The Art of Software Security Assessment
Mark Dowd, John McDonald, Justin Schuh.

Submission

Modifications