如果人工智能能在编程代码中找到漏洞，它也能在法律条文中做到这一点，从而为混乱铺平道路。斯坦福法学院 liftlab 访问研究员、法学学者 Felix Ringe 指出，大语言模型（LLM）已变得足够强大，能够大规模识别可被利用的不一致性，从而击垮法律体系。“法律体系以自然语言编码社会秩序的基础，”Ringe 与合作者 Linus Folkerts 上周在一篇博客文章中写道。
If AI can find the holes in programming code, it can do the same with legal code, paving the way for chaos. Felix Ringe, a visiting fellow at Stanford Law School’s liftlab and legal scholar, suggests large language models (LLMs) have become potent enough that they could break legal systems by identifying exploitable inconsistencies at scale. "Legal systems encode the foundations of social order in natural language," Ringe and collaborator Linus Folkerts argued in a blog post last week.
“与计算机系统类似，成文法可能包含招致利用的漏洞。”Ringe 主张，虽然法院倾向于通过法律解释来填补这些文本漏洞，但以自动化方式发现法律中可被对抗性利用的不一致性，可能会超出法律体系弥补差距的能力。
"Similar to computer systems, the written law can contain vulnerabilities that invite exploitation." Ringe contends that while courts tend to pave over these text vulnerabilities through statutory interpretation, the ability to discover adversarially useful inconsistencies in the law in an automated way risks overwhelming the legal system's ability to bridge the gaps.
为支持其论点，他引用了其他法学学者近期将法律视为易受攻击代码的研究成果，如《法律零日漏洞：先进 AI 系统的新型风险载体》和《法律体系中的硬件与软件漏洞》（PDF）。这一类比至少可追溯至 1999 年，即 Lawrence Lessig 的《代码：网络空间的法律及其他》（PDF）和《代码即法律：论网络空间的自由》。
To buttress his thesis, he points to recent work by other legal scholars that frames laws as vulnerable code, such as "Legal Zero-Days: A Novel Risk Vector for Advanced AI Systems" and "Hardware and Software Bugs in the Legal System" [PDF]. The analogy dates back at least to 1999, with Lawrence Lessig's Code and Other Laws of Cyberspace [PDF] and Code Is Law: On Liberty in Cyberspace.
Lessig 将“代码是网络空间的法律”这一想法归功于 William J. Mitchell 在《比特之城：空间、场所与信息高速公路》（1995）中的阐述，并补充说他“也从其他人那里借用了这一隐喻”。Ringe 则反其道而行之，提出法律即代码。
Lessig credits William J. Mitchell, in City of Bits: Space, Place, and the Infobahn (1995), with the idea that code is cyberspace's law, adding that he "drew the metaphor from others as well." Ringe has turned this on its head by proposing that law is code.
编程代码与法律代码之间的类比在一定程度上相当站得住脚。法律与代码都有各自的格式吹毛求疵和仪式感。当法院被比作运行时环境时，这一映射关系便失效了——非确定性的审判结果不适用于生产环境应用。尽管存在这些断裂点，Ringe 的观点是：AI 模型能够大规模揭示成文法中的缺口，其规模可能将软件安全生态系统中目前见到的混乱带入法律世界。
The analogy between programming code and legal code works reasonably well, up to a point. Law and code both have their share of formatting pedantry and ritual. The mapping breaks down when courts get cast as a runtime environment – non-deterministic trial outcomes don't work for production applications. Discontinuities aside, Ringe's point is that AI models can surface statutory gaps at a scale that may bring the chaos currently seen in the software security ecosystem to the legal world.
为了证明这一点，林格和福尔克茨引用了他们在大语言模型（LLM）帮助下发现的德国法律中存在的几个潜在漏洞，并指出他们仅分享那些他们认为不会造成严重危害的法律漏洞。例如，德国《职业病条例》（BKV）的一项修正案受宪法要求约束，必须引用其法定授权依据。但林格和福尔克茨认为，该修正案遗漏了所需的引用。
To demonstrate that, Ringe and Folkerts cite several potential gaps in German law that they uncovered with the help of LLMs, noting that they're only sharing legal exploits they believe could not cause serious harm. For example, an amendment to Germany's Occupational Diseases Regulation (BKV) is subject to a constitutional requirement to cite its statutory authority. But Ringe and Folkerts argue that the amendment omits a required reference.
因此，理论上，记者可以根据信息自由规则，请求获取提交给德国职业病咨询委员会的一份未公开声明，以此挑战研究人员认为可能无效的保密条款。另一个被引用的例子描述了一项过境签证规则，该规则可能因政府未能确定制定该规则的授权依据而无效。“我们认为，随着廉价法律推理的泛滥，AI系统可能会危及当今法律体系的运行，”他们写道。
So, in theory, a journalist could request an unpublished statement submitted to Germany's occupational-diseases advisory council under freedom of information rules, challenging a confidentiality provision that the researchers argue may be invalid. Another example cited describes a transit visa rule that may be invalid because the government failed to identify the authority under which the rule was made. "We believe that with cheap legal reasoning proliferating, AI systems could jeopardize the functioning of today’s legal systems," they wrote.
“在这样的世界里，立法者将需要大幅强化立法流程，例如利用先进的AI技术。”旧金山就业法律师、合伙人温迪·穆塞尔告诉《The》称，AI在法律领域的应用正在迅速扩大。“包括洛杉矶高等法院在内的加州一些法院，正在测试将AI用作法律助理，”穆塞尔说。“通常法律助理会通过研究和起草意见来协助法官审理案件。这意味着AI在某些法院正承担这一角色。诉讼律师现在必须考虑AI是如何分析动议和案件的。‘即使我个人发现不同的AI来源似乎存在倾向性或偏见，且往往无法准确分析法律问题，这也只是时间问题，AI终将介入立法进程。’”
"In such a world, lawmakers would need to significantly harden the process of legislation, for example by leveraging advanced AI themselves." Wendy Musell, a San Francisco-based employment law attorney and partner, told The that the use of AI in the legal field is expanding rapidly. "Some California Courts, including LA Superior Court, are testing the use of AI as law clerks," said Musell. "Generally law clerks would assist the Court by researching and drafting opinions for the judge's review. That means AI is serving that role in certain courts. Litigators now must consider how AI is analyzing motions and cases. "This is the case even where I personally have found different AI sources to appear to have a slant or bias, and often fail to accurately analyze legal issues. It was just a matter of time before AI came for the legislative process."
圣克拉拉大学法学院研究副院长、法学教授埃里克·戈德曼告诉《The》称，林格的论点有一定道理。
Eric Goldman, associate dean for research and professor of law at Santa Clara University School of Law, told The that Ringe's argument has some merit.
“我认为利用 AI 识别潜在的法定漏洞这一方法是合乎逻辑的，”戈德曼说。“不过，这需要仔细的人工审查，以确定 AI 识别出的漏洞是否真的是漏洞。法定漏洞也可能没有法律后果，或者至少没有重大后果；一旦被识别出来，立法机关通常会纠正任何重大漏洞。”戈德曼表示，立法者在起草过程中使用 AI 这一想法颇具雄心，至少在短期内如此。
"I think the methodology of using AI to identify potential statutory gaps is logical," said Goldman. "However, it will require careful human review to determine if the AI-identified gaps are in fact actually gaps. Statutory gaps also may have no legal consequence, or at least none of significance; and legislatures will often correct any major gaps once they are identified." Goldman said the idea that legislators should use AI during the drafting process is ambitious, at least in the short term.
“立法者起草法律时往往有各种目标和优先事项，避免法定漏洞很少是高优先级事项，”他解释道。“但许多立法机关都有立法顾问，负责将草案转化为最终的立法文本，我可以看到立法顾问可以从使用 AI 识别起草漏洞中受益，将其作为标准清理程序的一部分。” ®
"Legislators often draft laws with a range of goals and priorities, and avoiding statutory gaps is rarely a high priority," he explained. "But many legislatures have legislative counsel who are responsible for converting draft bills into final legislative text, and I could see where legislative counsel could benefit from using AI to identify drafting gaps as part of their standard cleanup protocols." ®