CVE-2020-17530 (GCVE-0-2020-17530)
Vulnerability from cvelistv5 – Published: 2020-12-11 01:11 – Updated: 2025-10-21 23:35Summary
Forced OGNL evaluation, when evaluated on raw user input in tag attributes, may lead to remote code execution. Affected software : Apache Struts 2.0.0 - Struts 2.5.25.
Severity
9.8 (Critical)
SSVC
Exploitation: active
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-02-06 20:53 UTC
CWE
- Remote Code Execution
- CWE-917 - Improper Neutralization of Special Elements used in an Expression Language Statement ('Expression Language Injection')
Assigner
References
12 references
| URL | Tags |
|---|---|
| https://cwiki.apache.org/confluence/display/WW/S2-061 | x_refsource_CONFIRM |
| http://jvn.jp/en/jp/JVN43969166/index.html | third-party-advisoryx_refsource_JVN |
| https://www.oracle.com/security-alerts/cpujan2021.html | x_refsource_MISC |
| http://packetstormsecurity.com/files/160721/Apach… | x_refsource_MISC |
| https://security.netapp.com/advisory/ntap-2021011… | x_refsource_CONFIRM |
| https://www.oracle.com/security-alerts/cpuApr2021.html | x_refsource_MISC |
| https://www.oracle.com//security-alerts/cpujul2021.html | x_refsource_MISC |
| https://www.oracle.com/security-alerts/cpuoct2021.html | x_refsource_MISC |
| https://www.oracle.com/security-alerts/cpujan2022.html | x_refsource_MISC |
| http://www.openwall.com/lists/oss-security/2022/04/12/6 | mailing-listx_refsource_MLIST |
| https://www.oracle.com/security-alerts/cpuapr2022.html | x_refsource_MISC |
| https://www.cisa.gov/known-exploited-vulnerabilit… | government-resource |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Apache Software Foundation | Apache Struts |
Affected:
Struts 2.0.0 - Struts 2.5.25
|
guessed |
CISA
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 1381e6c1-91b7-4dee-badb-912513c7ba04
Exploited: Yes
Timestamps
First Seen: 2021-11-03
Asserted: 2021-11-03
Scope
Notes: KEV entry: Apache Struts Remote Code Execution Vulnerability | Affected: Apache / Struts | Description: Forced Object-Graph Navigation Language (OGNL) evaluation in Apache Struts, when evaluated on raw user input in tag attributes, can lead to remote code execution. | Required action: Apply updates per vendor instructions. | Due date: 2022-05-03 | Known ransomware campaign use (KEV): Unknown | Notes (KEV): https://nvd.nist.gov/vuln/detail/CVE-2020-17530
Evidence
Type: Vendor Report
Signal: Successful Exploitation
Confidence: 80%
Source: cisa-kev
Details
| Cwes | CWE-917 |
|---|---|
| Feed | CISA Known Exploited Vulnerabilities Catalog |
| Product | Struts |
| Due Date | 2022-05-03 |
| Date Added | 2021-11-03 |
| Vendorproject | Apache |
| Vulnerabilityname | Apache Struts Remote Code Execution Vulnerability |
| Knownransomwarecampaignuse | Unknown |
References
Created: 2026-02-02 13:26 UTC
| Updated: 2026-02-06 07:53 UTC
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: b0520b2b-b57b-41f6-ad81-28fed72fbb37
Exploited: Yes
Timestamps
First Seen: 2021-11-03
Asserted: 2021-11-03
Scope
Notes: KEVIntel entry: Forced OGNL evaluation, when evaluated on raw user input in tag attributes, may lead to remote code execution. Affected software : Apache Struts... | Affected: Apache Software Foundation / Apache Struts | CVSS: 9.8 (CRITICAL) | Used in malware: unknown | Not yet in CISA KEV: False
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: kevintel
Details
| Feed | KEVIntel (kevintel.com) |
|---|---|
| Title | Forced OGNL evaluation, when evaluated on raw user input in tag attributes, may lead to remote code execution. Affected software : Apache Struts... |
| Vendor | Apache Software Foundation |
| Product | Apache Struts |
| Added Date | 2021-11-03T00:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | None |
| Cvss Severity | CRITICAL |
| Epss Percentile | None |
| Used In Malware | unknown |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | False |
References
Created: 2026-06-23 11:13 UTC
| Updated: 2026-06-23 11:13 UTC
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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