VulnerabilityPublished 16 Mar 2026

CVE-2026-4230: vanna · SQL Injection

Affects vannaSource CVE Database V5

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Public exploit available · CVSS 5.3 Medium.

Data as of 30 Oct 2026, 11:52 UTC

CVSS 4.0 base score

5.3Medium

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

EPSS: 0.2% chance of exploitation in 30 days – higher than 9% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

CVSS:4.0/AV:N/AC:L/AT:N/PR:L/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: Low
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: Low: The attacker requires privileges that provide basic capabilities that are typically limited to settings and resources owned by a single low-privileged user. Alternatively, an attacker with Low privileges has the ability to access only non-sensitive resources.
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

Immediately audit and review all code related to the update_sql function and any other database interaction points for proper input validation and sanitization.
Refactor vulnerable SQL queries to use parameterized statements or ORM-based query methods to prevent injection.
Restrict network access to the vulnerable endpoint by implementing firewall rules, VPNs, or IP whitelisting to limit exposure.
Deploy Web Application Firewalls (WAFs) with custom rules to detect and block SQL injection patterns targeting the affected endpoint.
Monitor database logs and application logs for unusual query patterns or errors indicative of injection attempts.
If possible, isolate the database with least privilege principles, ensuring the application account has minimal permissions.
Engage with the vendor or community to obtain or develop patches and apply them promptly once available.
Consider temporary mitigations such as disabling the vulnerable functionality if it is not critical to operations.
Educate development teams on secure coding practices to prevent similar vulnerabilities in future releases.

Description

A vulnerability has been found in vanna-ai vanna up to 2.0.2. Affected is the function update_sql of the file src/vanna/legacy/flask/__init__.py of the component Endpoint. Such manipulation leads to sql injection. The attack can be launched remotely. The exploit has been disclosed to the public and may be used. The vendor was contacted early about this disclosure but did not respond in any way.

Impact

The SQL injection vulnerability can lead to unauthorized disclosure, modification, or deletion of sensitive data stored in the backend database, severely impacting confidentiality and integrity. Availability may also be affected if attackers execute destructive queries or cause database errors. Since the vulnerability can be exploited remotely without authentication or user interaction, attackers can automate attacks at scale, increasing the risk of widespread compromise. Organizations relying on vanna-ai vanna for AI or analytics workloads may face data breaches, loss of trust, regulatory penalties, and operational disruptions. The absence of vendor patches and public exploit disclosures heighten the urgency for organizations to implement mitigations. The impact is particularly critical for entities handling sensitive or regulated data, such as financial, healthcare, or government sectors.

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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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