CVE-2025-3044VulnerabilityPublished 7 Jul 2025

CVE-2025-3044: run-llama/llama_index · Expected Behavior Violation

Affects run-llama/llama_indexSource CVE Database V5

Not known to be exploited · CVSS 5.3 Medium : upgrade to 0.12.28 when convenient.

Data as of 26 Oct 2026, 06:26 UTC

CVSS 3.0 base score

5.3Medium

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.3% chance of exploitation in 30 days – higher than 25% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch: Available – fixed in 0.12.28

CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:N

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: None
Integrity: Low
Availability: None

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: None: There is no loss of confidentiality within the impacted component.
Integrity: Low: Modification of data is possible, but the attacker does not have control over the consequence of a modification, or the amount of modification is limited. The data modification does not have a direct, serious impact on the impacted component.
Availability: None: There is no impact to availability within the impacted component.

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 7 Jul 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-26.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

Upgrade run-llama/llama_index to 0.12.28.

Affected software

Product	Affected	Fixed in
run-llama run-llama/llama_index	Affected <=0.12.22.post1 <0.12.28	Fixed in 0.12.28

AI-suggested

European organizations should promptly upgrade run-llama/llama_index to version 0.12.28 or later, where the vulnerability is fixed. Until the upgrade is applied, organizations can implement additional safeguards such as: 1) Modifying the filename generation logic to use collision-resistant hashing algorithms like SHA-256 instead of MD5, ensuring unique filenames for different papers. 2) Implementing checksums or content-based verification to detect and prevent overwriting files with different content. 3) Maintaining versioned backups of downloaded papers to recover any overwritten data. 4) Incorporating logging and alerting mechanisms to detect unexpected file overwrites during data ingestion. 5) Conducting regular audits of datasets to identify missing or corrupted files before training AI models. These measures will help maintain data integrity and minimize the risk of data loss until the patched version is deployed.

Description

A vulnerability in the ArxivReader class of the run-llama/llama_index repository, versions up to v0.12.22.post1, allows for MD5 hash collisions when generating filenames for downloaded papers. This can lead to data loss as papers with identical titles but different contents may overwrite each other, preventing some papers from being processed for AI model training. The issue is resolved in version 0.12.28.

Impact

For European organizations utilizing the run-llama/llama_index tool for AI model training, particularly those relying on academic paper datasets from sources like Arxiv, this vulnerability could result in incomplete or corrupted training datasets. This data integrity issue may lead to less accurate or biased AI models, which can affect research outcomes, product development, or decision-making processes that depend on these models. Organizations in academia, research institutions, and companies developing AI solutions could face setbacks in model performance or require additional verification steps to ensure dataset completeness. While the vulnerability does not expose sensitive data or cause system downtime, the loss of critical training data can have downstream effects on AI-driven services and products. Given the growing reliance on AI in various sectors across Europe, including healthcare, finance, and manufacturing, compromised training data integrity could indirectly impact operational effectiveness and innovation.

References

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 7 Jul 2025, 10:27 UTC

WeaknessesCWE-440

Technical details

References

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