CVE-2025-62498 (GCVE-0-2025-62498)
Vulnerability from cvelistv5 – Published: 2025-10-23 21:46 – Updated: 2025-10-24 14:34
VLAI
EPSS
VEX
Title
AutomationDirect Productivity Suite Relative Path Traversal
Summary
A relative path traversal (ZipSlip) vulnerability was discovered in Productivity Suite software version
4.4.1.19. The vulnerability allows an attacker who can tamper with a productivity project to execute arbitrary code on the machine where the project is opened.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-24 14:34 UTC
CWE
Assigner
References
Impacted products
8 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| AutomationDirect | Productivity Suite |
Affected:
0 , ≤ SW V4.2.1.9
(custom)
cpe:2.3:a:automationdirect:productivity_suite:*:*:*:*:*:*:*:* |
||
| AutomationDirect | Productivity 3000 P3-622 CPU |
Affected:
0 , ≤ SW V4.2.1.9
(custom)
cpe:2.3:a:automationdirect:productivity_3000_p3-622_cpu:*:*:*:*:*:*:*:* |
||
| AutomationDirect | Productivity 3000 P3-550E CPU |
Affected:
0 , ≤ SW V4.2.1.9
(custom)
cpe:2.3:a:automationdirect:productivity_3000_p3-550e_cpu:*:*:*:*:*:*:*:* |
||
| AutomationDirect | Productivity 3000 P3-530 CPU |
Affected:
0 , ≤ SW v4.4.1.19
(custom)
cpe:2.3:a:automationdirect:productivity_3000_p3-530_cpu:*:*:*:*:*:*:*:* |
||
| AutomationDirect | Productivity 2000 P2-622 CPU |
Affected:
0 , ≤ SW v4.4.1.19
(custom)
cpe:2.3:a:automationdirect:productivity_2000_p2-622_cpu:*:*:*:*:*:*:*:* |
||
| AutomationDirect | Productivity 2000 P2-550 CPU |
Affected:
0 , ≤ SW v4.4.1.19
(custom)
cpe:2.3:a:automationdirect:productivity_2000_p2-550_cpu:*:*:*:*:*:*:*:* |
||
| AutomationDirect | Productivity 1000 P1-550 CPU |
Affected:
0 , ≤ SW v4.4.1.19
(custom)
cpe:2.3:a:automationdirect:productivity_1000_p1-550_cpu:*:*:*:*:*:*:*:* |
||
| AutomationDirect | Productivity 1000 P1-540 CPU |
Affected:
0 , < SW v4.4.1.19
(custom)
cpe:2.3:a:automationdirect:productivity_1000_p1-540_cpu:*:*:*:*:*:*:*:* |
Date Public
2025-10-23 16:00
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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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