Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain
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AccessSupply attacker-controlled base_model_name_or_path
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ExploitOperator invokes AutoChem train routine
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ExecutionLoRA loader pulls untrusted model/code
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ImpactUntrusted functionality executed in training pipeline
Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A08:2025 - Software or Data Integrity Failures
ASVS 5.0
V15 - Secure Coding and Architecture Trusted source inclusion only
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| Exploitation Conditions | Exploitation requires the AutoChem 'train' function in tasks/AutoChem/code/experiment.py to be invoked with a base_model_name_or_path argument that the attacker can influence, causing the LoRA loading logic to include functionality from an untrusted control sphere. The limiting factor is the CVSS UI:P / user-interaction requirement: a legitimate operator must actually run the training routine with the malicious path value - the flaw does not trigger passively against an idle default deployment. No authentication is indicated (PR:N), but the precise mechanism by which base_model_name_or_path reaches the vulnerable call, and whether it yields full code execution, is not described in the available sources. |
| Impact | This is a moderate-severity issue, not an emergency. The supplied CVSS 4.0 vector (AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:L/VA:N, base 5.3) describes a network-reachable flaw that needs no privileges but DOES require passive user interaction (UI:P) - meaning an operator must actually invoke the training routine with the attacker-influenced argument. Impact is confined to low integrity (VI:L) with no confidentiality (VC:N) or availability (VA:N) effect, which is consistent with inclusion of untrusted functionality (CWE-829) rather than data theft. The vuln.today Decision label is 'this_month', signalling a scheduled-remediation priority rather than act_now/emergency. The exploit maturity is E:P (proof-of-concept): a public PoC reference exists but is not verified as working exploit code, so this should not be treated as confirmed active exploitation. There is no CISA KEV listing and no EPSS probability in the provided data, so widespread automated exploitation cannot be asserted. Note a signal conflict: the input Tag 'Information Disclosure' is inconsistent with the CVSS vector's VC:N (no confidentiality impact) - the actual impact is integrity via untrusted code/model inclusion, not disclosure; weight the vector over the tag. |
| Exploit ScenarioDerived | An attacker convinces or configures an InternAgent operator to run the AutoChem 'train' routine with a base_model_name_or_path value pointing at an attacker-controlled source, causing the LoRA loader to pull in functionality from an untrusted control sphere. A public PoC reference (poc.zip) exists but is not verified as working exploit code, and the CVSS UI:P requirement means the victim must initiate the training run for the attack to land. |
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Alert meAnalysisAIv1 – Oct 24, 2026
InternScience InternAgent 1.5.0 contains an untrusted-control-sphere inclusion flaw (CWE-829) in the LoRA loading logic reached through the 'train' function in tasks/AutoChem/code/experiment.py, where an attacker-influenced base_model_name_or_path value causes functionality to be pulled from outside the intended trust boundary. The assessed impact is limited to integrity (CVSS 3.1: 5.3, AV:N/AC:L/PR:N/UI:R, I:L only, no confidentiality or availability impact), and exploitation is not passive - a legitimate operator must run the training routine with the malicious path value. A public PoC reference exists; it is not verified as working exploit code, and there is no KEV confirmation of active exploitation; this is a moderate-severity issue slated for this month's remediation cycle rather than an emergency.
RemediationAI
No vendor-released patch version is identified in the available data; track the upstream issue https://github.com/InternScience/InternAgent/issues/30 and the project repository https://github.com/InternScience/InternAgent/ for a fixed release and upgrade when published. Until then, the effective control is to ensure base_model_name_or_path is never taken from untrusted or externally supplied input: restrict the AutoChem 'train' path to a hard-coded allowlist of vetted local model directories, validate and canonicalize paths before LoRA loading, and require a trusted operator to review the value before invoking tasks/AutoChem/code/experiment.py - noting that this trades away any flexibility users currently have to point training at arbitrary model locations. Because exploitation requires the training routine to be run with the malicious value, limit who can trigger training jobs and avoid running jobs generated from untrusted job specifications or third-party model references; the trade-off is reduced self-service access to the AutoChem workflow. Monitor the VulDB entries (https://vuldb.com/vuln/425984, https://vuldb.com/vuln/425984/cti) for updated fix information.
Affected VersionsENISA
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in euvd
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Technical ContextAI · show details
InternAgent is a research/agent framework used for automated experimentation; the AutoChem component wraps machine-learning training workflows, and its LoRA loading step accepts a base_model_name_or_path argument that names the model or path from which adapter weights and base-model functionality are loaded. CWE-829 (Inclusion of Functionality from Untrusted Control Sphere) describes the root cause: the code trusts a caller-supplied model path without confining it to a vetted set of sources, so a path pointing at an untrusted location brings foreign code or model artifacts into the training process. The relevant trigger is invocation of the 'train' function in tasks/AutoChem/code/experiment.py with an attacker-influenced base_model_name_or_path; the mechanism by which that value reaches the vulnerable call, and whether it escalates to full code execution, is not described in the available sources. CPE cpe:2.3:a:internscience:internagent:*:*:*:*:*:*:*:* indicates the product family is affected broadly across versions at or around 1.5.0. The supplied CVSS 4.0 vector (E:P) and the reference-only public PoC both point to proof-of-concept-level verification rather than confirmed exploitation.
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Severity by source
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N/E:P
An operator must run training with the attacker-influenced base_model_name_or_path, so UI:R and no privileges; only integrity is affected by untrusted functionality inclusion, so C and A are N.
Primary rating from Vendor (VulDB).
CVSS VectorVendor: VulDB
Lifecycle Timeline
3DescriptionCVE.org
A weakness has been identified in InternScience InternAgent 1.5.0. This affects the function train of the file tasks/AutoChem/code/experiment.py of the component LoRA Loading. This manipulation of the argument base_model_name_or_path causes inclusion of functionality from untrusted control sphere. The attack may be initiated remotely. The exploit has been made available to the public and could be used for attacks.
Threat Intelligence Overview
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.
EUVD-2026-109220
GHSA-cgw3-x9m5-7cc6
NVD
CVE.org
EPSS
VulDB
Exploit-DB