The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could be interpreted as escape, meta, or control character sequences when they are sent to a downstream component.

What CVEs are caused by CWE-150?

51 recorded CVEs are attributed to CWE-150, including CVE-2023-26055, CVE-2022-30123, CVE-2020-6932.

How is CWE-150 detected?

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.)

What are the consequences of CWE-150?

Exploiting CWE-150 can lead to: Execute Unauthorized Code or Commands, Hide Activities, Unexpected State.

Is CWE-150 actively exploited?

51 recorded CVEs are caused by CWE-150; none are currently in CISA's KEV catalog of actively exploited flaws.

CWE-150: Improper Neutralization of Escape, Meta, or Control Sequences (ANSI injection)

How to prevent it

Practical mitigations for CWE-150, grouped by where in the lifecycle they apply.

Developers should anticipate that escape, meta and control characters/sequences will be injected/removed/manipulated in the input vectors of their product. Use an appropriate combination of denylists and allowlists to ensure only valid, expected and appropriate input is processed by the system.

Implementation

Input Validation

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.

Implementation

Output Encoding

While it is risky to use dynamically-generated query strings, code, or commands that mix control and data together, sometimes it may be unavoidable. Properly quote arguments and escape any special characters within those arguments. The most conservative approach is to escape or filter all characters that do not pass an extremely strict allowlist (such as everything that is not alphanumeric or white space). If some special characters are still needed, such as white space, wrap each argument in quotes after the escaping/filtering step. Be careful of argument injection (CWE-88).

Implementation

Input Validation

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.

Implementation

When using output from an LLM, neutralize or strip escape codes before redirecting output to the terminal or other rendering engine that would process the codes. The neutralization could require that the character be printable and/or allowable whitespace, such as a carriage return or newline. Be deliberate about what to allow.

Effectiveness: High — By restricting output to only printable characters, this effectively removes any escape codes.

Build and Compilation

When using an LLM: during tokenizer training, suppress escape codes from the tokenizer's vocabulary. Depending on context, this could be accomplished by removing the codes from input to the tokenizer, or removing the map from the string to its token ID. It is generally unlikely that this removal would adversely affect the quality or correctness of what is generated, e.g. advice requests for terminal settings to change colors.

Effectiveness: Limited — This could raise the exploitation effort for an adversary, but the adversary might still be able to get string together multiple tokens in order to form the escape code.

CWE-150: Improper Neutralization of Escape, Meta, or Control Sequences

Overview

Real-world CVEs

Common consequences

How it happens

When it is introduced

Applies to

How to prevent it

How to detect it

Automated Static Analysis

Code examples

Illustrative examples

Terminology & mappings

Alternate terms

Mapped taxonomies

Attack patterns

Frequently asked questions

References

Stay ahead of CWE-150

Overview

Real-world CVEs

Common consequences

How it happens

When it is introduced

Applies to

How to prevent it

How to detect it

Automated Static Analysis

Code examples

Illustrative examples

Related weaknesses

Parent

Terminology & mappings

Alternate terms

Mapped taxonomies

Attack patterns

Frequently asked questions

References

Stay ahead of CWE-150