Floating Point Comparison with Incorrect Operator

Floating-point arithmetic is inherently imprecise due to how computers represent decimal numbers in binary. Operations like addition, multiplication, or division can introduce microscopic rounding errors. This means two calculations that should mathematically yield the same result might produce values that differ by an extremely small amount (e.g., 1.0000000000000002 vs. 1.0). A direct equality check will falsely treat these as different numbers. This reliability flaw can cause software to behave unpredictably—such as failing validation checks, entering incorrect logic branches, or crashing. If an attacker can influence the calculations being compared, they might exploit this instability to bypass security controls, cause a denial of service, or trigger unexpected program behavior.