InternScience InternAgent LoRA Loading experiment.py train inclusion of functionality from untrusted control sphere
- Published
- Last modified
- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean? →
- published
- Whether CISA lists it in its Known Exploited Vulnerabilities catalog: proof that attackers are using it.What do these mean? →
- Not listed in CISA KEV
- Whether working attack code is public (Exploit-DB, Metasploit, Nuclei or GitHub). Public code makes attacks easier.What do these mean? →
- None on Exploit-DB, Metasploit or GitHub
- Exploitation: poc
- Automatable: no
- Technical impact: partial
Summary
InternScience InternAgent, an AI research agent framework, is affected by a flaw in its LoRA model loading component, where a user-supplied base model path can cause the software to load and run code from an untrusted source. An attacker could potentially exploit this remotely, which may allow them to run unauthorized code or compromise the system.
Summarized by anthropic/claude-haiku-5.5. Verify details against the original sources.
Read the original description
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.
Timeline
- CVE published
- First seen by cve.now
- CISA SSVC assessment
Exploitation: poc · Automatable: no · Technical impact: partial
- Record last updated
Affected versions
- InternScience InternAgent
1.5.0