CVE-2026-78378 (GCVE-0-2026-78378)
Vulnerability from cvelistv5 – Published: 2026-08-24 13:19 – Updated: 2026-08-24 15:20
VLAI
EPSS
VEX
Title
Redis Glob Pattern Injection Allows Unauthorized Enumeration of Private Ransomlook Data
Summary
Ransomlook contains a Redis glob pattern injection vulnerability caused by insufficient neutralization of user-controlled input before it is incorporated into Redis SCAN MATCH patterns.
The /api/health/<name> endpoint attempted to resolve the supplied name to a known group or market, but when resolution failed it fell back to using the attacker-controlled value directly in a Redis key pattern. An unauthenticated attacker could therefore supply Redis glob metacharacters such as *, ?, [ or ] to broaden the SCAN operation beyond the intended group. For example, requesting /api/health/* could enumerate health information, mirror slugs, and uptime series belonging to all groups and markets, including entities marked as private.
Similar unsafe interpolation was present in /api/crypto/chain/<chain> and in the delete_manual_torrent() function. The latter represents a potentially destructive sink because a crafted infohash containing glob metacharacters could cause the scan to match torrent-health keys belonging to other torrents if attacker-controlled input can reach that function.
The patch removes the unsafe fallback from the health endpoint and introduces glob escaping for user-controlled values before they are incorporated into Redis SCAN MATCH expressions.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-24 15:20 UTC
CWE
- CWE-200 - Exposure of Sensitive Information to an Unauthorized Actor
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/RansomLook/RansomLook/commit/1… | patch |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| ransomlook | ransomlook |
Affected:
0 , ≤ 2.0.0
(semver)
|
guessed |
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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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