VulnerabilityPublished 16 Mar 2026

CVE-2026-4229: vanna · SQL Injection

Affects vannaSource CVE Database V5

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Public exploit available · CVSS 6.9 Medium.

Data as of 30 Oct 2026, 11:52 UTC

CVSS 4.0 base score

6.9Medium

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

EPSS: 0.2% chance of exploitation in 30 days – higher than 18% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P

Base metrics

Attack Vector: Network
Attack Complexity: Low
Attack Requirements: None
Privileges Required: None
User Interaction: None
Vulnerable system confidentiality: Low
Vulnerable system integrity: Low
Vulnerable system availability: Low
Subsequent system confidentiality: None
Subsequent system integrity: None
Subsequent system availability: None

Threat metrics

Exploit Maturity: POC

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

Attack Vector: Network: The vulnerable system is bound to the network stack and the set of possible attackers extends beyond the other options listed below, 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: The attacker must take no measurable action to exploit the vulnerability. The attack requires no target-specific circumvention to exploit the vulnerability. An attacker can expect repeatable success against the vulnerable system.
Attack Requirements: None: The successful attack does not depend on the deployment and execution conditions of the vulnerable system. The attacker can expect to be able to reach the vulnerability and execute the exploit under all or most instances of the vulnerability.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files of the vulnerable system to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without interaction from any human user, other than the attacker.
Vulnerable system confidentiality: Low: There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to the Vulnerable System.
Vulnerable system 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 to the Vulnerable System.
Vulnerable system availability: Low: Performance is reduced or there are interruptions in resource availability. Even if repeated exploitation of the vulnerability is possible, the attacker does not have the ability to completely deny service to legitimate users. The resources in the Vulnerable System are either partially available all of the time, or fully available only some of the time, but overall there is no direct, serious consequence to the Vulnerable System.
Subsequent system confidentiality: None: There is no loss of confidentiality within the Subsequent System or all confidentiality impact is constrained to the Vulnerable System.
Subsequent system integrity: None: There is no loss of integrity within the Subsequent System or all integrity impact is constrained to the Vulnerable System.
Subsequent system availability: None: There is no impact to availability within the Subsequent System or all availability impact is constrained to the Vulnerable System.
Exploit Maturity: POC: Based on threat intelligence sources each of the following must apply: · Proof-of-concept is publicly available · No knowledge of reported attempts to exploit this vulnerability · No knowledge of publicly available solutions used to simplify attempts to exploit the vulnerability

Definitions: FIRST CVSS v4.0 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 16 Mar 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: evidence found.
CVSS exploit maturity: proof of concept public
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), CVSS vector, the Metasploit, Exploit-DB and nuclei-templates index as of 30 Oct 2026.

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.
Weaponized: A Metasploit exploit module or Exploit-DB entry exists (Metasploit scanner modules do not count), or CISA SSVC or the CVSS exploit maturity 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

Affected software

Product	Affected	Fixed in
vanna-aivanna	Affected =2.0.0 =2.0.1 =2.0.2	Fixed in–
PyPIvanna-ai/vanna`pkg:pypi/vanna-ai/vanna`Other threats	Affected =2.0.0 =2.0.1 =2.0.2	Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

Given the absence of official patches, organizations should implement immediate compensating controls. These include deploying web application firewalls (WAFs) with custom rules to detect and block SQL injection patterns targeting the remove_training_data function or the ID parameter. Network segmentation and restricting access to the vanna service to trusted IP addresses can reduce exposure. Monitoring and logging all requests to the vulnerable endpoint can help detect exploitation attempts early. If feasible, temporarily disabling or restricting the remove_training_data functionality until a patch is available can mitigate risk. Additionally, organizations should review and sanitize all inputs at the application level, applying strict validation and parameterization to prevent injection. Regularly updating threat intelligence feeds and monitoring for any emerging exploits or vendor patches is critical. Finally, consider isolating the database with least privilege access to limit the impact of any successful injection.

Description

A flaw has been found in vanna-ai vanna up to 2.0.2. This impacts the function remove_training_data of the file src/vanna/legacy/google/bigquery_vector.py. This manipulation of the argument ID causes sql injection. The attack can be initiated remotely. The exploit has been published and may be used. The vendor was contacted early about this disclosure but did not respond in any way.

Impact

The SQL injection vulnerability in vanna-ai vanna can have significant consequences for organizations relying on this software. Successful exploitation can lead to unauthorized disclosure of sensitive data, unauthorized modification or deletion of training data, and potential disruption of service availability. This can compromise the integrity of machine learning models or analytics relying on the training data, leading to incorrect outputs or decisions. Since the vulnerability is remotely exploitable without authentication, attackers can target exposed instances over the network, increasing the risk of widespread attacks. The lack of vendor response and patches further elevates the risk, as organizations must rely on their own defenses. Data breaches resulting from this vulnerability could lead to regulatory penalties, reputational damage, and operational disruptions, especially in sectors where data integrity and confidentiality are critical.

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 16 Mar 2026, 09:05 UTC

Technical details

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