Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:NSummary
CVE-2026-27170 is a high-severity Improper Input Validation (CWE-20) vulnerability in Opensift Opensift. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 8th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and SA-11 (Developer Testing and Evaluation) — 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.
CVE-2026-27170 affects OpenSift, an AI study tool that processes large datasets via semantic search and generative AI. In versions 1.1.2-alpha and prior, the URL ingest feature exhibits overly permissive server-side fetch behavior, allowing coercion into requesting unsafe targets. This enables potential access or probing of private or local network resources directly from the OpenSift host process when processing attacker-controlled URLs. The vulnerability is classified under CWE-20 (Improper Input Validation) and CWE-918 (Server-Side Request Forgery), with a CVSS v3.1 base score of 7.1 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:N).
An attacker with low privileges, such as an authenticated user, can exploit this over the network with low complexity and no user interaction required. By supplying malicious URLs during ingestion, they can compel the OpenSift server to fetch resources from internal private networks or local services, achieving high confidentiality impact through unauthorized data access or reconnaissance, alongside low integrity impact.
The issue is addressed in OpenSift version 1.1.3-alpha. For mitigation, practitioners should upgrade immediately. As a cautious workaround for trusted local-only exceptions, set the environment variable OPENSIFT_ALLOW_PRIVATE_URLS=true. Additional details are available in the GitHub release notes at https://github.com/OpenSift/OpenSift/releases/tag/v1.1.3-alpha and the security advisory at https://github.com/OpenSift/OpenSift/security/advisories/GHSA-3w2r-hj5p-h6pp.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-7745
Vulnerability Data
OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. In versions 1.1.2-alpha and below, URL ingest allows overly permissive server-side fetch behavior and can be coerced into requesting unsafe targets. Potential access/probing…
more
of private/local network resources from the OpenSift host process when ingesting attacker-controlled URLs. This issue has been fixed in version 1.1.3-alpha. To workaround when using trusted local-only exceptions, use OPENSIFT_ALLOW_PRIVATE_URLS=true with caution.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, generative ai
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 6 hardening rules · 3 OS baselines
V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Developer testing and evaluation can discover missing input validation through analysis or test cases.
SI-10 directly requires validity checks on information inputs, structurally preventing improper or missing validation.
Requiring documented development standards and tools can embed input-validation practices into the engineering process.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
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.
Secure SDLC practices directly require and enforce input validation during development.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Testing against a defined set of requirements and using code review plus vulnerability scanning forces validation of inputs and handling of unanticipated conditions, reducing the chance that malformed data will be accepted.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.
Secure-coding guidelines and mandatory security testing (including code scans) compel developers to validate and sanitize inputs at design and implementation time, lowering the incidence of malformed or malicious data reaching downstream components.
Mandating input controls that include integrity checks and input validation ensures that untrusted data is examined before use, blocking the root cause of many injection and malformed-data weaknesses.
Security-by-design principles explicitly call for data validation and sanitization at every layer, reducing the chance that malformed or malicious input will be processed without scrutiny.
Requiring language-specific secure coding standards, peer review, SAST and documented mitigation of common programming errors forces validation of all inputs before they are trusted.
Hardening callouts derived
Configuration rules from DISA STIG baselines that bear on weaknesses of the type cited by this CVE. Each rule is shown with the relationship its mapping actually records, against the CWE it was authored against. Derived via CVE→CWE over `controls_xwalks` (authoritative rows only; rows rated `none` are excluded).
RHEL 8 (1 rule)
- V-230265 RHEL 8 must prevent the installation of software, patches, service packs, device drivers, or operating system components of local packages without verification they have been digitally signed using a certificate that is issued by a Certificate Authority (CA) that is recognized and approved by the organization. prevents CWE-20