A classy cyberattack marketing campaign has emerged, exploiting a crucial vulnerability in Langflow, a widely-used Python-based framework for constructing AI purposes, to deploy the damaging Flodrix botnet.
Recognized as CVE-2025-3248 and carrying a near-perfect CVSS rating of 9.8, this unauthenticated distant code execution (RCE) flaw impacts Langflow variations previous to 1.3.0.
Unveiling a Extreme RCE Vulnerability in Langflow
The vulnerability lies within the /api/v1/validate/code endpoint, which lacks correct enter validation and sandboxing, enabling attackers to execute arbitrary Python code with minimal effort by way of a crafted POST request.
In accordance with the Report, this ease of exploitation has led to fast weaponization, with over 1,600 internet-exposed Langflow cases in danger globally, as reported by cybersecurity specialists at Pattern Micro.
The flaw’s severity and Langflow’s reputation evidenced by over 70,000 GitHub stars make it a primary goal for malicious actors searching for to compromise methods and disrupt companies.
The assault chain begins with attackers utilizing reconnaissance instruments like Shodan and FOFA to determine susceptible Langflow servers uncovered on public networks.
Leveraging publicly accessible proof-of-concept (PoC) exploits hosted on GitHub, these cybercriminals achieve distant shell entry, executing instructions resembling whoami and ip addr present to assemble system intelligence.
This data is relayed to a command-and-control (C&C) server, after which a malicious downloader script, usually disguised as “docker,” retrieves and installs the Flodrix botnet payload over TCP or the Tor community.
A Stealthy Risk with Devastating Capabilities
An evolution of the LeetHozer malware household, Flodrix is engineered for distributed denial-of-service (DDoS) assaults, supporting modes like tcpraw and udpplain.
Its superior evasion techniques, together with self-deletion except particular parameters are met, XOR-based string obfuscation to cover C&C addresses, and misleading youngster course of naming, make it a formidable adversary.
Moreover, Flodrix avoids reinfection by checking for hidden recordsdata like .system_idle and wipes forensic traces, posing important challenges to detection and mitigation.
The malware’s twin functionality for DDoS assaults and potential knowledge exfiltration heightens the danger of service disruption and delicate data theft for organizations counting on Langflow for clever automation.
To counter this menace, speedy motion is crucial. Organizations should improve to Langflow model 1.3.0 or later, which contains authentication by way of a _current_user: CurrentActiveUser parameter to safe the susceptible endpoint.
Additional protecting measures embody limiting public entry to Langflow interfaces and vigilantly monitoring for indicators of compromise, resembling anomalous community site visitors or surprising hidden recordsdata.
This marketing campaign highlights the pressing want for well timed patching and strong community safety within the face of evolving botnet threats exploiting open-source frameworks.
Indicators of Compromise (IOCs)
Under are the hashes related to Flodrix samples recognized by PolySwarm:
| SHA256 Hash |
|---|
| AB0F9774CA88994091DB0AE328D98F45034F653BD34E4F5E85679A972D3A039C |
| C2BCDD6E3CC82C4C4DB6AAF8018B8484407A3E3FCE8F60828D2087B2568ECCA4 |
| EC52F75268B2F04B84A85E08D56581316BD5CCFEB977E002EB43270FE713F307 |
| CCB02DCE1BCA9C3869E1E1D1774764E82206026378D1250AED324F1B7F9B1F11 |
| F73B554E6AA7095CFC79CDB687204D99533AEDA73309106BA6CC9428FF57BD1E |
| EE84591092A971C965B4E88CC5D6E8C2F07773B3BEE1486F3A52483EE72A2B3B |
| 002F3B2C632E0BE6CBC3FDF8AFCD0432FFE36604BA1BA84923CADAA147418187 |
| 99B59E53010D58F47D332B683EB8A40DF0E0EACEF86390BCA249A708E47D9BAD |
| 08CF20E54C634F21D8708573EEF7FDE4DBD5D3CD270D2CB8790E3FE1F42ECCEC |
| 6DD0464DD0ECDE4BB5A769C802D11AB4B36BBE0DD4F0F44144121762737A6BE0 |
| C462A09DB1A74DC3D8ED199EDCA97DE87B6ED25C2273C4A3AFE811ED0C1C8B1D |
| 9850EB26D8CBEF3358DA4DF154E054759A062116C2AA82DE9A69A8589F0DCE49 |
| A42F8428AA75C180C2F89FBB8B1E44307C2390ED0EBF5AF10015131B5494F9E1 |
| ABB0C4AD31F013DF5037593574BE3207A4C1E066A96E58CE243AAF2EF0FC0E4D |
| DF9E9006A566A4FE30EAA48459EC236D90FD628F7587DA9E4A6A76D14F0E9C98 |
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