CVE-2020-4463 (GCVE-0-2020-4463)
Vulnerability from cvelistv5 – Published: 2020-07-29 14:05 – Updated: 2024-09-16 21:02Summary
IBM Maximo Asset Management 7.6.0.1 and 7.6.0.2 is vulnerable to an XML External Entity Injection (XXE) attack when processing XML data. A remote attacker could exploit this vulnerability to expose sensitive information or consume memory resources. IBM X-Force ID: 181484.
Severity
CWE
- Obtain Information
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://www.ibm.com/support/pages/node/6253953 | x_refsource_CONFIRM |
| https://exchange.xforce.ibmcloud.com/vulnerabilit… | vdb-entryx_refsource_XF |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| IBM | Maximo Asset Management |
Affected:
7.6.0.1
Affected: 7.6.0.2 |
Date Public
2020-07-28 00:00
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 9dc364b7-14a2-4d61-954d-9dcc908bb3fe
Exploited: Yes
Timestamps
First Seen: 2025-08-31
Asserted: 2025-08-31
Scope
Notes: IBM Maximo Asset Management 7.6.0.1 and 7.6.0.2 is vulnerable to an XML External Entity Injection (XXE) attack when processing XML data. A remote... | Affected: IBM / Maximo Asset Management | CVSS: 8.2 (HIGH) | EPSS: 0.3159 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | IBM Maximo Asset Management 7.6.0.1 and 7.6.0.2 is vulnerable to an XML External Entity Injection (XXE) attack when processing XML data. A remote... |
| Cve Id | CVE-2020-4463 |
| Vendor | IBM |
| Ghsa Id | None |
| Product | Maximo Asset Management |
| Added Date | 2025-08-31T00:00:00.000Z |
| Cvss Score | 8.2 |
| Epss Score | 0.3159 |
| Previous Ids | |
| Cvss Severity | HIGH |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.9824 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2020-4463 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:09 CEST
| Updated: 2026-10-02 09:09 CEST
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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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