Liability for the Generation of Malicious Code: Delineating Responsibility Between the AI Model Creator and the User Who Circumvented Its Restrictions

Introduction

The rapid implementation of digital technologies is blurring the traditional boundaries between human and machine. Today, algorithms create highly complex software products, write texts, and generate visual images. In this regard, the relevance of the problem of ownership rights to content generated by artificial intelligence is increasing every day. Who legally owns the digital object — the developer of the neural network or the user who formulated a precise text prompt? The issue of authorship instantly moves from the realm of abstract theory into harsh legal reality when, instead of a useful product, malicious code is generated [3, p. 82]. It is precisely here that the problem of ownership rights is most closely intertwined with tort obligations: recognizing a subject as the owner or author of the generated result inevitably entails placing the burden of responsibility on them for the consequences of using this code.

However, the current legal framework turns out to be unprepared for such radical technological challenges. Hence, a clear problem statement is formed: how to fairly and legally accurately delineate responsibility between the creator of an intelligent system and the end user who purposefully bypassed built-in security filters to generate a computer virus? Classical doctrines of civil law are historically oriented towards subjects endowed with free will and consciousness [7, p. 14]. When a self-learning algorithm is involved in the process of creating a malicious object, a pronounced «liability gap» emerges [5, p. 817]. Regulations aimed at the large-scale support of artificial intelligence technologies in the economy [9, art. 2] do not yet provide an exhaustive answer to the question of the culpability of the parties in the event of the intentional hacking of protective barriers (the so-called jailbreak). In essence, it is necessary to answer an extremely complex legal question: is the successful circumvention of restrictions solely the malicious intent of the user, or did the developer initially release a vulnerable product into civil circulation, for the flaws of which they must bear strict legal responsibility [2, p. 47].

Legal Grounds for Liability for the Generation and Use of Malicious Code

The analysis of the legal grounds for liability for generating malicious code using algorithms requires a deep dive into the current norms of civil legislation. The basic principles of compensation for harm, laid down in the Civil Code of the Russian Federation, were initially designed to regulate relations exclusively between individuals and legal entities [1, art. 410]. However, the introduction of self-learning neural networks into the process of creating software products breaks the familiar paradigm. In situations where an algorithm acts as a powerful computational tool for writing a computer virus, traditional mechanisms for establishing guilt fail. A specific «liability gap» is formed, which manifests itself most vividly and destructively when analyzing complex cyber incidents involving fully autonomous systems [5, p. 818].

The key difficulty lies in the legal qualification of the very process of generating malicious digital content. Obviously, artificial intelligence lacks independent legal personality, so any attempts to hold the algorithm itself legally liable are completely devoid of practical sense [3, p. 83]. The attention of legal scholars is focused on finding the guilty party among the people and corporations involved in the operation of the system. Existing legal mechanisms for distributing responsibility offer several theoretical approaches [2, p. 46]. The basic concept relies on the strict guilt of the end user: if a person purposefully forms a malicious prompt, bypasses protective restrictions, and ignores the rules of operation, they are recognized as the main subject of the offense.

At the same time, it is absolutely impossible to exclude the creator of the neural network from the chain of responsibility. In doctrine, the possibility of applying the concept of a source of increased danger to modern artificial intelligence technologies is actively discussed [10, p. 56]. If an algorithm released into circulation initially contains critical architectural vulnerabilities that allow bypassing built-in security filters without much effort, liability can be imposed on the developer for releasing a defective product. Foreign experience is also interesting in this context. For example, through the prism of the concept of French civil liability, there is a clear trend of applying the liability regime for harm caused by things [6, p. 35]. According to this approach, the creator or owner of an «intellectual thing» bears the burden of proving the absence of their guilt if the generated code caused damage to third parties.

In essence, current legislation, including federal norms on supporting the development of artificial intelligence technologies [9, art. 2], sets only general framework conditions for the digital economy. Issues of a detailed and fair delineation of guilt for cyber incidents currently remain outside the scope of strict regulatory control. Attempts to apply classical liability models to the facts of illegal use of neural networks only reveal an acute need for the development of specialized legal regimes [12, p. 47]. Ultimately, effective legal counteraction against threats emanating from generated malicious code requires not just a targeted adaptation of old codes, but the formation of qualitatively new legal grounds capable of fairly dividing the burden of responsibility between an imprudent developer and a malicious user [7, p. 15].

The specifics of using intelligent systems require a differentiated approach in other industries as well. Thus, in forensic activity, the use of algorithms is associated with the highest risks of violating citizens’ rights in case of incorrect analysis of digital traces [4, p. 46]. The autonomy of programs here must be strictly controlled, and the final decisions must be verified by a human [8, p. 176]. Similar challenges are observed in the context of the digitalization of the educational environment, where the use of neural networks requires transparency and the possibility of challenging the results [13, p. 12]. In general, legal liability in the field of artificial intelligence requires a deep theoretical rethinking [11, p. 22].

Delineation of Liability in the Intentional Circumvention of Neural Network Restrictions

The issue of distributing legal guilt acquires maximum urgency when a user purposefully overcomes the protective algorithms of a system to create malicious software. The intentional circumvention of restrictions (jailbreak) is a conscious act aimed at exploiting the vulnerabilities of a neural network. From the perspective of classical legal liability models, it is the user who becomes the key initiator of the offense in this situation [7, p. 16]. By formulating specific prompts, they force the algorithm to ignore embedded ethical and technical barriers. Consequently, a direct causal link is established between the intent of the end operator and the resulting cyber incident [5, p. 821].

However, completely exempting the creator of the intelligent system from liability seems legally incorrect. Legal mechanisms for distributing the burden of damage compensation require a detailed analysis of the developer’s actions during the model’s training and testing phase [2, p. 48]. If the neural network architecture has critical flaws that allow blocks to be removed by entering the simplest text commands, a solid ground arises for recognizing the manufacturer’s guilt. Legal science actively examines theoretical models that allow for joint liability in cases where the creator released a knowingly vulnerable digital product into civil circulation [10, p. 57]. Negligence in designing security filters is practically equated to the release of a product of inadequate quality, which inevitably entails corresponding tort obligations [3, p. 84].

The key criterion for a fair delineation of guilt is the level of effort exerted by the hacker and the degree of objective reliability of the original security systems. In cases where the user applies highly technical and non-standard circumvention methods that objectively could not have been foreseen during development, the entire weight of guilt is entirely shifted to the initiator of the malicious prompt [12, p. 49]. In essence, the developer cannot bear absolute responsibility for every unlawful act committed using their software if all reasonable precautions were taken and current security standards were considered at the creation stage.

Ultimately, solving the «liability gap» problem requires a flexible and differentiated approach. Legal liability for generating malicious code must be clearly divided: the end user answers for criminal intent and the direct application of the generated object, while the AI model creator answers for technical negligence and the lack of appropriate basic protection mechanisms. Only such a distribution of roles allows maintaining a fragile balance between protecting public interests and stimulating the further development of the artificial intelligence industry.

Conclusion

The rapid development of neural network technologies has exposed serious gaps in existing legal regulation. The problem of distributing guilt for the generation of malicious code during the intentional circumvention of an algorithm’s restrictions does not have a definitive solution within the framework of classical civil law [1, art. 410]. In essence, the legal system faces the need to adapt to conditions where an intelligent program acts not just as an ordinary tool, but as a complex independent link in the chain of causing harm [2, p. 49].

A fair delineation of legal liability requires a comprehensive approach that takes into account both the malicious intent of the end operator and the degree of prudence of the developer [5, p. 823]. If bypassing security filters (jailbreak) became possible due to critical negligence at the stage of designing and training the model, guilt should be distributed jointly [10, p. 58]. However, in situations where the initiator applies high-tech, non-standard, and unpredictable methods of hacking an initially reliable architecture, the burden of compensation for damages is entirely shifted to the malicious actor [7, p. 18].

Ultimately, to effectively counter cyber threats and eliminate the «liability gap», the immediate formation of specialized legislation is required. It is necessary to develop clear technical and legal security standards for creators of AI models [9, art. 4], while not forgetting about strict legal sanctions for users who intentionally turn a useful digital product into a destructive weapon [3, p. 85]. Only a carefully calibrated balance between state quality control of algorithms and support for innovation will ensure the safe and sustainable development of the digital economy [12, p. 51].