CVE-2017-8046 (GCVE-0-2017-8046)
Vulnerability from cvelistv5 – Published: 2018-01-04 06:00 – Updated: 2024-08-05 16:19Summary
Malicious PATCH requests submitted to servers using Spring Data REST versions prior to 2.6.9 (Ingalls SR9), versions prior to 3.0.1 (Kay SR1) and Spring Boot versions prior to 1.5.9, 2.0 M6 can use specially crafted JSON data to run arbitrary Java code.
Severity
9.8 (Critical)
CWE
- run arbitrary Java code
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://pivotal.io/security/cve-2017-8046 | x_refsource_CONFIRM |
| https://access.redhat.com/errata/RHSA-2018:2405 | vendor-advisoryx_refsource_REDHAT |
| http://www.securityfocus.com/bid/100948 | vdb-entryx_refsource_BID |
| https://www.exploit-db.com/exploits/44289/ | exploitx_refsource_EXPLOIT-DB |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Pivotal | Pivotal Spring Data REST and Spring Boot |
Affected:
Pivotal Spring Data REST versions prior to 2.6.9 (Ingalls SR9), 3.0.1 (Kay SR1) and Spring Boot versions prior to 1.5.9, 2.0 M6
|
guessed |
Date Public
2018-01-04 00:00
Shadowserver
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 521f3632-6899-449d-9e75-b34d6f2947a6
Exploited: Yes
Characteristics
Severity:
98.0
Timestamps
First Seen: 2024-01-06
Asserted: 2024-01-06
Last Seen: 2026-08-17
Scope
Asset Exposure: ['internet-facing']
Notes: Affected: Spring / Spring Data REST | Class: other-software | Severity: Critical (CVSS 9.8) | IoT: no | In CISA KEV: no | Honeypot connections on 2026-08-17: 1
Evidence
Type: Honeypot
Signal: In The Wild Attempts
Confidence: 70%
Source: shadowserver
Details
| 1D | 1 |
|---|---|
| Iot | no |
| Feed | Shadowserver Foundation honeypot/exploited-vulnerabilities |
| Type | http-scan |
| Class | other-software |
| 7D Avg | 0 |
| Vendor | Spring |
| 30D Avg | 0 |
| 90D Avg | 0 |
| Product | Spring Data REST |
| Cisa Kev | no |
| Connections | 1 |
| Observation Date | 2026-08-17 |
| Vulnerability Class | CVSS |
| Vulnerability Score | 9.8 |
| Vulnerability Severity | Critical |
References
Created: 2026-07-01 07:18 UTC
| Updated: 2026-08-19 01:00 UTC
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 9aec72a2-cc39-42ea-bf8b-484b501bed66
Exploited: Yes
Timestamps
First Seen: 2018-01-04
Asserted: 2018-01-04
Scope
Notes: Malicious PATCH requests submitted to servers using Spring Data REST versions prior to 2.6.9 (Ingalls SR9), versions prior to 3.0.1 (Kay SR1) and... | Affected: Pivotal / Pivotal Spring Data REST and Spring Boot | CVSS: 9.8 (CRITICAL) | EPSS: 0.7453 | 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 | Malicious PATCH requests submitted to servers using Spring Data REST versions prior to 2.6.9 (Ingalls SR9), versions prior to 3.0.1 (Kay SR1) and... |
| Cve Id | CVE-2017-8046 |
| Vendor | Pivotal |
| Ghsa Id | GHSA-9QF9-28H9-HQCJ |
| Product | Pivotal Spring Data REST and Spring Boot |
| Added Date | 2018-01-04T06:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | 0.7453 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99498 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2017-8046 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-11-04 13:00 UTC
| Updated: 2026-11-04 13:00 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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
How durations are calculated
Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.
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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
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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.
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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.