Sabre is a defense to adversarial examples that was accepted at IEEE S&P
2024. We first reveal significant flaws in the evaluation that point to clear
signs of gradient masking. We then show the cause of this gradient masking: a
bug in the original evaluation code. By fixing a single line of code in the
original repository, we reduce Sabre's robust accuracy to 0%. In response to
this, the authors modify the defense and introduce a new defense component not
described in the original paper. But this fix contains a second bug; modifying
one more line of code reduces robust accuracy to below baseline levels.

通过揭示在评估过程中的明显的梯度掩蔽问题和代码中的漏洞，我们展示了防御对抗性样本的 Sabre 算法的缺陷，并通过修复一个代码错误，将其鲁棒准确率降至 0%。作者通过修改算法引入新的防御组件以应对此问题，但修复过程中出现了第二个错误，进一步降低了算法的鲁棒准确率。

修复一行代码破解 Sabre 漏洞

Cutting through buggy adversarial example defenses: fixing 1 line of  code breaks Sabre

Often, a good explanation for a program's unexpected behavior is a bug in the
programmer's code. But sometimes, an even better explanation is a bug in the
programmer's mental model of the language they are using. Instead of merely
debugging our current code ("giving the programmer a fish"), what if our tools
could directly debug our mental models ("teaching the programmer to fish")? In
this paper, we apply ideas from computational cognitive science to do exactly
that. Given a perplexing program, we use program synthesis techniques to
automatically infer potential misconceptions that might cause the user to be
surprised by the program's behavior. By analyzing these misconceptions, we
provide succinct, useful explanations of the program's behavior. Our methods
can even be inverted to synthesize pedagogical example programs for diagnosing
and correcting misconceptions in students.

使用计算认知科学的方法，我们利用程序合成技术来自动推断可能导致用户对程序行为感到惊讶的误解，以此提供简明实用的解释。甚至可以将我们的方法倒转，用于合成示范性教学程序，以诊断和纠正学生的误解。