Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:N/SC:H/SI:H/SA:H/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2025-59352 is a medium-severity Path Traversal (CWE-22) vulnerability in Linuxfoundation Dragonfly. Its CVSS base score is 6.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 49th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to AC-23 (Data Mining Protection) and AC-3 (Access Enforcement) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
Dragonfly is an open source P2P-based file distribution and image acceleration system. Prior to version 2.1.0, its gRPC API and HTTP APIs contain path traversal flaws (CWE-22 and CWE-202) that let any peer issue requests forcing another peer to write files to arbitrary locations on its filesystem and to read arbitrary files from it. The affected component is the peer-to-peer communication layer used for content distribution.
Any participating peer can exploit the issue without authentication to exfiltrate secret data such as credentials or tokens stored on other peers and to achieve remote code execution on those peers by writing executable content into locations that will later be run. The CVSS 4.0 score of 6.9 reflects the network-accessible attack vector combined with high impact on confidentiality, integrity, and availability of the targeted peer.
The vulnerability is fixed in Dragonfly 2.1.0. The project’s security advisory GHSA-79hx-3fp8-hj66 and the accompanying comprehensive security report both recommend upgrading immediately; no other mitigations such as network segmentation or API authentication changes are described in the references.
The EPSS score remains flat at 0.0184 with no material increase after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-29768
Vulnerability Data
Dragonfly is an open source P2P-based file distribution and image acceleration system. Prior to 2.1.0, the gRPC API and HTTP APIs allow peers to send requests that force the recipient peer to create files in arbitrary file system locations, and…
more
to read arbitrary files. This allows peers to steal other peers’ secret data and to gain remote code execution (RCE) capabilities on the peer’s machine.This vulnerability is fixed in 2.1.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V5.3.2
Mitigating Controls (NIST 800-53 r5) AI
AC-23 directly requires mechanisms to protect against unauthorized data mining and inference from statistical queries that would expose sensitive information.
Enforces the intended directory access authorizations that path traversal would otherwise bypass.
Input validation directly neutralizes special path elements before pathname construction occurs.
Least privilege reduces the impact of any unauthorized file access obtained via traversal.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Least-privilege query permissions directly limit the data an attacker can request or infer.
Behavior analytics on query activity can detect inference attempts but does not prevent exposure at query time.
Protecting data-in-use reduces what remains available for inference via queries.
Patching/maintenance can remediate known path-traversal flaws in deployed software (partial prevention of exploitability) but does nothing to stop the coding defect from being introduced in the first place.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Access control limits who can run queries that could expose sensitive information via inference.
Granular access rights reduce the ability of users to craft inference queries.
Data masking prevents inference by obscuring sensitive values returned in query results.
Security testing in development catches path traversal via static/dynamic analysis.
Information access restriction directly limits query scope that could lead to inference.
Classification helps identify sensitive data that must be protected from inference attacks.