Security readout for executives and security teams
Plain-English summary
A flaw in Google langfun can let an unauthenticated remote attacker turn crafted prompt input into Python code executed by the host application. Successful exploitation could expose, alter, or destroy application data and take over the affected process. Risk depends on whether the default lf.query Python protocol receives untrusted input.
Executive priority
Treat internet-facing or multi-tenant affected deployments as an immediate remediation priority because compromise could grant the application’s full privileges. Rapidly identify exposed lf.query usage, upgrade, and restrict untrusted input. Lower priority is reasonable only after confirming the vulnerable protocol is absent or unreachable.
Technical view
The default lf.query Python protocol may evaluate model-generated Python expressions without sandboxing. Crafted prompts can influence the model’s output and reach this evaluation path, causing arbitrary code execution with the host application’s privileges. The CVSS 4.0 score is 9.2; the vector records a required attack condition but no privileges or user interaction.
Likely exposure
Highest exposure exists where langfun versions before 0.1.2 use the default lf.query Python protocol with internet-originated or otherwise untrusted prompts. The structured affected entry specifically lists 0.0.1 while the description says versions before 0.1.2; organizations should verify exact versions and usage rather than relying solely on package presence.
Exploitation context
The supplied record supports remote, unauthenticated code execution, but exploitation depends on crafted input causing suitable model output. The CVE is not listed as KEV, and the supplied sources provide no evidence of active exploitation, public exploit availability, or confirmed attacks.
Researcher notes
CWE-95 directly describes eval injection; CWE-1188 is also assigned. Version evidence needs careful interpretation: the narrative covers versions before 0.1.2, while the structured affected list names 0.0.1 and defaults other versions to unaffected. No exploit indicators, attack samples, sandbox details, or forensic signatures are supplied.
Mitigation direction
- Upgrade langfun to version 0.1.2 or later, following current Google guidance.
- Until upgraded, prevent untrusted prompts from reaching affected lf.query workflows.
- Run affected applications with minimal filesystem, network, credential, and service privileges.
- Review Google langfun issue 725 for updated remediation or compatibility guidance.
Validation and detection
- Inventory installed langfun versions across applications, images, notebooks, and deployment lockfiles.
- Identify workflows using the default lf.query Python protocol.
- Trace whether unauthenticated or untrusted prompt input can reach those workflows.
- Confirm deployed environments run langfun 0.1.2 or later.
- Verify untrusted prompt content cannot reach dynamically evaluated Python expressions.
- Review telemetry for unexpected Python execution associated with prompt-processing activity.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Conservative CVE-to-ATT&CK context
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-1188: Exact CWE lookup
Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCWE-95: Code execution behavior lookup
Code execution and unsafe deserialization weaknesses often justify reviewing execution behavior and process telemetry. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCVE-2026-75062 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
Open ATT&CK lookup- Severity
- Critical
- CVSS
- 9.2 (4.0)
- Known Exploited
- No
- Published
Vector: CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
CNA and ADP enrichment extracted from CVE v5
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
SSVC decision data
CVSS vector scores
1 official scoreWe collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N——GoogleVulnerability scoring details
Base CVSS 4.0 score
9.2CriticalVector: CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
Vulnerability timeline
Timeline events are normalized from CVE metadata, CNA source timelines, ADP timelines, and KEV metadata when present.
- CVE reservedCVE Program
The CVE ID was reserved by the assigning CNA.
- CVE publishedCVE Program
The CVE record was published.
- CVE updatedCVE Program
The CVE record metadata indicates this as the latest update time.
ADP provider summaries
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/google/langfunCVE reference, Google · product
- https://github.com/google/langfun/issues/725CVE reference, Google · issue-tracking
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
Initialization of a Resource with an Insecure Default
Initialization of a Resource with an Insecure Default represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.
Improper Neutralization of Directives in Dynamically Evaluated Code
Improper Neutralization of Directives in Dynamically Evaluated Code represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.