Apache Druid Remote Code Execution (RCE) Scanner

Apache Druid is an open-source, distributed data store that is used for high-performance real-time data processing and analysis. It is commonly utilized by organizations requiring low-latency queries, often in business intelligence, analytics, and data warehouse scenarios. Due to its capabilities, Druid is widely adopted across various industries, such as finance, retail, and telecommunications, to streamline and enhance data analytics processes. The software is leveraged by developers and data scientists to handle large volumes of event data efficiently. Furthermore, Druid is built to scale effectively, supporting both batch and real-time data ingestion. Its popularity stems from its impressive query performance and operational simplicity.

The vulnerability in question is a Remote Code Execution (RCE) flaw, specifically tied to Apache Log4j, a logging utility used by Apache Druid. This security hole allows an attacker to execute arbitrary code on the affected system remotely without authorization, leading to a severe security compromise. Exploiting this vulnerability can be achieved by sending a specially crafted request that the Log4j component mishandles. The issue resides in how Log4j processes logging inputs, allowing external manipulation. Various applications using vulnerable versions of Log4j are impacted, thereby making this vulnerability critical. The presence of such a vulnerability underscores the broader risks associated with libraries or components with widespread adoption.

Technical details of the vulnerability indicate that the vulnerable endpoint includes the path pattern dealing with lookups in the Druid coordinator service. The payload injected involves a crafted JNDI (Java Naming and Directory Interface) lookup, triggering interaction with an external server via a DNS request. Critical parameters involve the JNDI string, which is susceptible to exploitation for malicious purposes, facilitating attackers in gaining remote code execution abilities. The vulnerability detection aligns closely with observed interaction responses and specific header or status codes. Detecting such a flaw necessitates careful crafting of requests to identify abnormal or compromised responses indicative of successful exploitation attempts. Security professionals often simulate these conditions to assess risks efficiently, leveraging controlled environments.

Exploiting this vulnerability can lead to detrimental outcomes, including unauthorized remote access to execute malicious code that could alter, steal, or delete sensitive data. Once an attacker gains control, they might use compromised systems as pivot points for further attacks within the network. The impacts might extend to severe operational disruptions, including denial-of-service conditions. Financial losses and reputational damage are subsequent effects that organizations face amidst data breaches stemming from such vulnerabilities. Mitigation becomes a paramount concern to prevent data leaks, maintain system integrity, and protect user privacy. Maintaining regular audits and updates of vulnerable components can reduce potential exploit risks significantly.

REFERENCES

• https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44228