CVE-2017-3066 (GCVE-0-2017-3066)
Vulnerability from cvelistv5 – Published: 2017-04-27 14:00 – Updated: 2025-10-21 23:55Summary
Adobe ColdFusion 2016 Update 3 and earlier, ColdFusion 11 update 11 and earlier, ColdFusion 10 Update 22 and earlier have a Java deserialization vulnerability in the Apache BlazeDS library. Successful exploitation could lead to arbitrary code execution.
Severity
9.8 (Critical)
SSVC
Exploitation: active
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-02-24 15:18 UTC
CWE
- Code Injection
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://www.exploit-db.com/exploits/43993/ | exploitx_refsource_EXPLOIT-DB |
| https://helpx.adobe.com/security/products/coldfus… | x_refsource_CONFIRM |
| http://www.securityfocus.com/bid/98003 | vdb-entryx_refsource_BID |
| http://www.securitytracker.com/id/1038364 | vdb-entryx_refsource_SECTRACK |
| https://www.cisa.gov/known-exploited-vulnerabilit… | government-resource |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| n/a | Adobe ColdFusion ColdFusion 2016 Update 3 and earlier, ColdFusion 11 update 11 and earlier, ColdFusion 10 Update 22 and earlier |
Affected:
Adobe ColdFusion ColdFusion 2016 Update 3 and earlier, ColdFusion 11 update 11 and earlier, ColdFusion 10 Update 22 and earlier
|
Date Public
2017-04-27 00:00
CISA
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 5074caa4-8a63-4bb0-a0b8-d4fdfceffc29
Exploited: Yes
Timestamps
First Seen: 2025-02-24
Asserted: 2025-02-24
Scope
Notes: Adobe ColdFusion Deserialization Vulnerability | Affected: Adobe / ColdFusion | Description: Adobe ColdFusion contains a deserialization vulnerability in the Apache BlazeDS library that allows for arbitrary code execution. | Required action: Apply mitigations per vendor instructions or discontinue use of the product if mitigations are unavailable. | Due date: 2025-03-17 | Known ransomware campaign use (KEV): Unknown | Notes (KEV): https://helpx.adobe.com/security/products/coldfusion/apsb17-14.html ; https://nvd.nist.gov/vuln/detail/CVE-2017-3066
Evidence
Type: Vendor Report
Signal: Successful Exploitation
Confidence: 80%
Source: cisa-kev
Details
| Cwes | CWE-502 |
|---|---|
| Feed | CISA Known Exploited Vulnerabilities Catalog |
| Product | ColdFusion |
| Due Date | 2025-03-17 |
| Date Added | 2025-02-24 |
| Vendorproject | Adobe |
| Vulnerabilityname | Adobe ColdFusion Deserialization Vulnerability |
| Knownransomwarecampaignuse | Unknown |
References
Created: 2026-10-02 08:09 CEST
| Updated: 2026-10-02 08:09 CEST
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 16897f84-82a5-4475-b94d-d020fff59df0
Exploited: Yes
Timestamps
First Seen: 2025-02-24
Asserted: 2025-02-24
Scope
Notes: Adobe ColdFusion 2016 Update 3 and earlier, ColdFusion 11 update 11 and earlier, ColdFusion 10 Update 22 and earlier have a Java deserialization... | Affected: Adobe / Adobe ColdFusion ColdFusion 2016 Update 3 and earlier, ColdFusion 11 update 11 and earlier, ColdFusion 10 Update 22 and earlier | CVSS: 9.8 (CRITICAL) | EPSS: 0.90597 | Used in malware: unknown | Not yet in CISA KEV: False
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | Adobe ColdFusion 2016 Update 3 and earlier, ColdFusion 11 update 11 and earlier, ColdFusion 10 Update 22 and earlier have a Java deserialization... |
| Cve Id | CVE-2017-3066 |
| Vendor | Adobe |
| Ghsa Id | None |
| Product | Adobe ColdFusion ColdFusion 2016 Update 3 and earlier, ColdFusion 11 update 11 and earlier, ColdFusion 10 Update 22 and earlier |
| Added Date | 2025-02-24T00:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | 0.90597 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99801 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2017-3066 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | False |
References
Created: 2026-10-02 09:11 CEST
| Updated: 2026-10-02 09:11 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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