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Weakness · ClassCWE-20

CWE-20: Improper Input Validation

Likelihood of exploit: HighKEV-linkedClass

As of 2026-10-05, CWE-20 (Improper Input Validation) underlies 66 CVEs tracked by Threadlinqs, 12 of them in the CISA Known Exploited Vulnerabilities catalog, and is cited by 154 tracked threats. MITRE rates its likelihood of exploit as High.

CVEs
66Mapped to CWE-20
CISA KEV
12Exploited in the wild
Critical
23CVSS v3 critical CVEs
Threats
154Tracked campaigns citing it
Likelihood
HighMITRE likelihood of exploit

Last updated:

What is CWE-20?

The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

Input validation is a frequently-used technique for checking potentially dangerous inputs in order to ensure that the inputs are safe for processing within the code, or when communicating with other components. Input can consist of: raw data - strings, numbers, parameters, file contents, etc. metadata - information about the raw data, such as headers or size Data can be simple or structured. Structured data can be composed of many nested layers, composed of combinations of metadata and raw data, with other simple or structured data. Many properties of raw data or metadata may need to be validated upon entry into the code, such as: specified quantities such as size, length, frequency, price, rate, number of operations, time, etc. implied or derived quantities, such as the actual size of a file instead of a specified size indexes, offsets, or positions into more complex data structures symbolic keys or other elements into hash tables, associative arrays, etc. well-formedness, i.e. syntactic correctness - compliance with expected syntax lexical token correctness - compliance with rules for what is treated as a token specified or derived type - the actual type of the input (or what the input appears to be) consistency - between individual data elements, between raw data and metadata, between references, etc. conformance to domain-specific rules, e.g. business logic equivalence - ensuring that equivalent inputs are treated the same authenticity, ownership, or other attestations about the input, e.g. a cryptographic signature to prove the source of the data Implied or derived properties of data must often be calculated or inferred by the code itself. Errors in deriving properties may be considered a contributing factor to improper input validation.

CWE-20 is a class-level weakness in MITRE’s Common Weakness Enumeration, with a MITRE likelihood of exploit of High. Applicable platforms: Language: Not Language-Specific.

Source: MITRE CWE (CWE-20 definition, reproduced verbatim). Counts and linkage below are Threadlinqs data.

Consequences

  • Availability — DoS: Crash, Exit, or Restart, DoS: Resource Consumption (CPU), DoS: Resource Consumption (Memory). An attacker could provide unexpected values and cause a program crash or arbitrary control of resource allocation, leading to excessive consumption of resources such as memory and CPU.
  • Confidentiality — Read Memory, Read Files or Directories. An attacker could read confidential data if they are able to control resource references.
  • Integrity, Confidentiality, Availability — Modify Memory, Execute Unauthorized Code or Commands. An attacker could use malicious input to modify data or possibly alter control flow in unexpected ways, including arbitrary command execution.

Source: MITRE CWE, common consequences.

How CWE-20 is exploited in the wild

Threadlinqs maps 66 CVEs to CWE-20, published between 2017-03-03 and 2026-10-03. 12 are listed in CISA’s Known Exploited Vulnerabilities catalog, the authoritative record of exploitation in the wild, and 4 are tied to ransomware campaigns. By CVSS v3 severity the set splits into 23 critical, 26 high, 13 medium, 1 low. The highest EPSS score in the set is 99.9% (CVE-2024-3400), the modelled probability of exploitation in the next 30 days. 154 tracked threats reference CWE-20 directly or through a CVE it covers; the most recent is “Multiple Vulnerabilities in Microsoft Edge prior to 154.0.4258.53 (HK GovCERT A26-10-03)” (2026-10-02). Affected products concentrate in Google (8), Microsoft (8), Zoom Communications (3), among 44 vendors in total.

Vulnerabilities (CVEs)

Showing 40 of 66 CVEs mapped to CWE-20, CISA KEV first, then by CVSS score.

  • CVE-2024-3400 — CISA KEV · CVSS 10 critical · EPSS 99.9% · published 2024-04-12
  • CVE-2021-44228 — CISA KEV · CVSS 10 critical · EPSS 94.3% · published 2021-12-10
  • CVE-2026-93952 — CISA KEV · CVSS 10 critical · EPSS 1.0% · published 2026-09-22
  • CVE-2023-23397 — CISA KEV · CVSS 9.8 critical · EPSS 93.3% · published 2023-03-14
  • CVE-2024-21413 — CISA KEV · CVSS 9.8 critical · EPSS 92.9% · published 2024-02-13
  • CVE-2026-12569 — CISA KEV · CVSS 9.8 critical · EPSS 1.1% · published 2026-06-18
  • CVE-2026-34197 — CISA KEV · CVSS 8.8 high · EPSS 84.1% · published 2026-04-01
  • CVE-2015-2291 — CISA KEV · CVSS 7.8 high · EPSS 4.8% · published 2017-08-09
  • CVE-2023-41061 — CISA KEV · CVSS 7.8 high · EPSS 0.9% · published 2023-09-07
  • CVE-2018-0171 — CISA KEV · CVSS 7.5 high · EPSS 99.5% · published 2018-03-28
  • CVE-2026-6973 — CISA KEV · CVSS 7.2 high · EPSS 4.9% · published 2026-05-07
  • CVE-2021-38000 — CISA KEV · CVSS 6.1 medium · EPSS 4.5% · published 2021-11-23
  • CVE-2026-21858 — CVSS 10 critical · EPSS 6.6% · published 2026-01-08
  • CVE-2026-48277 — CVSS 10 critical · EPSS 0.8% · published 2026-06-30
  • CVE-2026-48281 — CVSS 10 critical · EPSS 0.8% · published 2026-06-30
  • CVE-2026-34910 — CVSS 10 critical · EPSS 0.1% · published 2026-05-22
  • CVE-2016-7407 — CVSS 9.8 critical · EPSS 5.5% · published 2017-03-03
  • CVE-2026-65637 — CVSS 9.8 critical · EPSS 0.5% · published 2026-08-25
  • CVE-2026-7808 — CVSS 9.8 critical · EPSS 0.3% · published 2026-08-23
  • CVE-2026-20093 — CVSS 9.8 critical · EPSS 0.0% · published 2026-04-01
  • CVE-2026-53412 — CVSS 9.8 critical · published 2026-07-16
  • CVE-2024-23469 — CVSS 9.6 critical · EPSS 10.5% · published 2024-07-17
  • CVE-2026-85047 — CVSS 9.6 critical · EPSS 0.4% · published 2026-09-03
  • CVE-2026-33000 — CVSS 9.1 critical · EPSS 0.0% · published 2026-05-22
  • CVE-2026-59568 — CVSS 9.1 critical · published 2026-08-24
  • CVE-2026-11386 — CVSS 9 critical · EPSS 0.3% · published 2026-07-16
  • CVE-2026-32157 — CVSS 9 critical · EPSS 0.1% · published 2026-04-14
  • CVE-2026-33844 — CVSS 9 critical · EPSS 0.0% · published 2026-05-07
  • CVE-2026-16723 — CVSS 9 critical · published 2026-07-23
  • CVE-2017-17215 — CVSS 8.8 high · EPSS 78.6% · published 2018-03-20
  • CVE-2023-36899 — CVSS 8.8 high · EPSS 70.0% · published 2023-08-08
  • CVE-2026-52876 — CVSS 8.8 high · EPSS 0.1% · published 2026-08-18
  • CVE-2026-33114 — CVSS 8.8 high · EPSS 0.0% · published 2026-04-14
  • CVE-2025-71399 — CVSS 8.6 high · EPSS 0.3% · published 2026-08-02
  • CVE-2023-3466 — CVSS 8.3 high · EPSS 1.1% · published 2023-07-19
  • CVE-2026-52877 — CVSS 8.3 high · EPSS 0.3% · published 2026-08-18
  • CVE-2026-14428 — CVSS 8.3 high · EPSS 0.2% · published 2026-07-01
  • CVE-2026-14429 — CVSS 8.3 high · EPSS 0.2% · published 2026-07-01
  • CVE-2026-73658 — CVSS 8.2 high · EPSS 0.3% · published 2026-08-13
  • CVE-2026-65604 — CVSS 8.2 high · published 2026-07-23

Affected vendors

Threat activity

154 tracked threats cite CWE-20; the 25 most recent are listed.

Mitigations

  • Architecture and Design / Attack Surface Reduction: Consider using language-theoretic security (LangSec) techniques that characterize inputs using a formal language and build "recognizers" for that language. This effectively requires parsing to be a distinct layer that effectively enforces a boundary between raw input and internal data representations, instead of allowing parser code to be scattered throughout the program, where it could be subject to errors or inconsistencies that create weaknesses. [REF-1109] [REF-1110] [REF-1111]
  • Architecture and Design / Libraries or Frameworks: Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).
  • Architecture and Design, Implementation / Attack Surface Reduction: Understand all the potential areas where untrusted inputs can enter the product, including but not limited to: parameters or arguments, cookies, anything read from the network, environment variables, reverse DNS lookups, query results, request headers, URL components, e-mail, files, filenames, databases, and any external systems that provide data to the application. Remember that such inputs may be obtained indirectly through API calls.
  • 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…
  • Architecture and Design: For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server. Even though client-side checks provide minimal benefits with respect to server-side security, they are still useful. First, they can support intrusion detection. If the server receives input that should have been rejected by the client, then it may be an indication of an attack. Second, client-side error-checking…
  • Implementation: When your application combines data from multiple sources, perform the validation after the sources have been combined. The individual data elements may pass the validation step but violate the intended restrictions after they have been combined.
  • Implementation: Be especially careful to validate all input when invoking code that crosses language boundaries, such as from an interpreted language to native code. This could create an unexpected interaction between the language boundaries. Ensure that you are not violating any of the expectations of the language with which you are interfacing. For example, even though Java may not be susceptible to buffer overflows, providing a large argument in a call to native code might trigger an overflow.
  • Implementation: Directly convert your input type into the expected data type, such as using a conversion function that translates a string into a number. After converting to the expected data type, ensure that the input's values fall within the expected range of allowable values and that multi-field consistencies are maintained.
  • Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control. Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
  • Implementation: When exchanging data between components, ensure that both components are using the same character encoding. Ensure that the proper encoding is applied at each interface. Explicitly set the encoding you are using whenever the protocol allows you to do so.

Source: MITRE CWE, potential mitigations.

Detection methods (MITRE CWE)

  • Automated Static Analysis: Some instances of improper input validation can be detected using automated static analysis. A static analysis tool might allow the user to specify which application-specific methods or functions perform input validation; the tool might also have built-in knowledge of validation frameworks such as Struts. The tool may then suppress or de-prioritize any associated warnings. This allows the analyst to focus on areas of the software in which input validation does not appear to be present. Except…
  • Manual Static Analysis: When custom input validation is required, such as when enforcing business rules, manual analysis is necessary to ensure that the validation is properly implemented.
  • Fuzzing: Fuzzing techniques can be useful for detecting input validation errors. When unexpected inputs are provided to the software, the software should not crash or otherwise become unstable, and it should generate application-controlled error messages. If exceptions or interpreter-generated error messages occur, this indicates that the input was not detected and handled within the application logic itself.
  • Automated Static Analysis - Binary or Bytecode (effectiveness: SOAR Partial): According to SOAR [REF-1479], the following detection techniques may be useful: Cost effective for partial coverage: Bytecode Weakness Analysis - including disassembler + source code weakness analysis Binary Weakness Analysis - including disassembler + source code weakness analysis
  • Manual Static Analysis - Binary or Bytecode (effectiveness: SOAR Partial): According to SOAR [REF-1479], the following detection techniques may be useful: Cost effective for partial coverage: Binary / Bytecode disassembler - then use manual analysis for vulnerabilities & anomalies
  • Dynamic Analysis with Automated Results Interpretation (effectiveness: High): According to SOAR [REF-1479], the following detection techniques may be useful: Highly cost effective: Web Application Scanner Web Services Scanner Database Scanners
  • Dynamic Analysis with Manual Results Interpretation (effectiveness: High): According to SOAR [REF-1479], the following detection techniques may be useful: Highly cost effective: Fuzz Tester Framework-based Fuzzer Cost effective for partial coverage: Host Application Interface Scanner Monitored Virtual Environment - run potentially malicious code in sandbox / wrapper / virtual machine, see if it does anything suspicious
  • Manual Static Analysis - Source Code (effectiveness: High): According to SOAR [REF-1479], the following detection techniques may be useful: Highly cost effective: Focused Manual Spotcheck - Focused manual analysis of source Manual Source Code Review (not inspections)

Source: MITRE CWE, detection methods. Threadlinqs detection rules for the threats above are Blue tier and higher.