Second-order necessary conditions are criteria used in optimization to determine whether a candidate solution is a local extremum. They involve the second derivative (or Hessian matrix in multivariable cases) and ensure that the solution is not just a saddle point but a true minimum or maximum, provided the first-order conditions are also satisfied.