EUVD-2026-66505
GHSA-8x83-862f-c85r
Severity by source
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/E:X/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:X
Network-reachable and unauthenticated (AV:N/PR:N), but AC:H because success depends on inducing the model to emit executable Python; unsandboxed eval yields full C/I/A with no scope change.
Primary rating from Vendor (Google).
CVSS VectorVendor: Google
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/E:X/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:X
Lifecycle Timeline
3DescriptionCVE.org
Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection') in the default lf.query Python protocol in Google langfun versions prior to 0.1.2 allows remote unauthenticated attackers to execute arbitrary Python code in the context of the host application via crafted prompt inputs that cause the model to generate executable Python expressions evaluated without a sandbox.
Articles & Coverage 1
AnalysisAI
Remote code execution in Google langfun before 0.1.2 arises because the default lf.query Python protocol evaluates model-generated Python expressions with no sandbox, letting remote unauthenticated attackers run arbitrary code in the host application by supplying crafted prompt inputs that steer the model into emitting executable code. Because langfun embeds directly in LLM-driven Python applications, any deployment that pipes untrusted prompt content through lf.query is exposed. …
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Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata
Vulnerability AssessmentAI
| Exploitation | Exploitation requires the target application to use langfun (version < 0.1.2) with its default lf.query Python protocol - the code-generating/eval path - and to route attacker-influenced content into that query. … Additional conditions and limiting factors are described in the full assessment. |
| Risk Assessment | The signals are largely consistent and point to a genuine high priority, with one important nuance. … Full risk analysis with EPSS, KEV, and SSVC signal comparison available after sign-in. |
| Exploit Scenario | An attacker submits a crafted prompt to an application that forwards user input into langfun's default lf.query Python protocol; the input coaxes the backing model into emitting a Python expression containing malicious code (for example an os.system or import call), which langfun evaluates without a sandbox and thereby executes on the server. No authentication is required (PR:N) and the attack is network-borne (AV:N), though success depends on the model actually producing the executable payload (AT:P). … |
| Remediation | Vendor-released patch: 0.1.2 - upgrade langfun to 0.1.2 or later, which is the primary and authoritative fix (pin the dependency and rebuild any images or environments that vendored an older release). … Detailed patch versions, workarounds, and compensating controls in full report. |
Recommended ActionAI
Within 24 hours, inventory all systems using Google langfun and identify deployments where lf.query processes untrusted user prompts, prioritizing customer-facing or sensitive-data applications. …
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Same weakness CWE-95 – Eval Injection
View allSame technique Code Injection
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External POC / Exploit Code
Leaving vuln.today