Floating-point arithmetic represents and calculates with numbers that may include fractions, very large values, or very small values. A number is stored approximately using a significand and an exponent, so many decimal values cannot be represented exactly. This can cause rounding differences or small errors in calculations.
It matters in cybersecurity because software often uses floating-point calculations for measurements, simulations, graphics, and scientific workloads. Unexpected rounding or precision loss can affect security checks, cryptographic implementations, or decisions based on calculated thresholds. Attackers may exploit such flaws when they lead to incorrect validation or unsafe behavior. Defenders should use suitable numeric types, account for rounding, and test boundary cases. Specialized hardware, such as a floating-point coprocessor, can perform these calculations efficiently, but does not change their precision limits.