Databom

Databom is a term used in cybersecurity discourse to describe a data-centric attack payload embedded in data that aims to disrupt or degrade the integrity, confidentiality, or availability of database systems when processed by an application or database management system. The term is not universally standardized and commonly appears in security research discussions, risk assessments, and fictional contexts to illustrate data-driven attack concepts.

In its conceptual model, a databom is triggered by the intake or processing of certain data items—such as a particular record, field value, or batch—where the payload exploits weaknesses in input validation, deserialization, or processing logic. The effects can range from data corruption, erroneous updates, to cascading changes across related records, potentially bypassing normal validation if safeguards are insufficient. Because databoms operate via data channels, they resemble other data integrity threats such as injection or deserialization flaws but are framed specifically as payloads encoded within data itself.

Impact and prevalence: In practice, databoms are discussed mainly as hypothetical or scenario-based concerns rather than as widely observed attack classes. They are used in threat modeling to emphasize the importance of data integrity controls and end-to-end validation. They may appear in fiction or illustrative security exercises to demonstrate how trusted data inputs can undermine systems if not properly sanitized.

Defense and mitigation: Strengthen input validation and data schema constraints; use least-privilege access and robust authentication; implement transactional integrity and foreign key constraints; employ checksums, cryptographic signing of data, and tamper-evident logging; maintain frequent backups and tested recovery procedures; monitor for anomalous data patterns and enforce end-to-end data provenance.

See also: Data integrity, Database security, Input validation, Deserialization vulnerability, Data breach.