Malware Generation Vulnerability
Updated: May 5, 2026
Description
The AI model can be manipulated into generating malware or performing malicious actions.
This vulnerability arises when an attacker uses carefully crafted prompts to instruct the model to generate harmful code, exploit vulnerabilities, or guide the execution of malicious tasks.
Example Attack
If exploited, this vulnerability could allow attackers to use the AI model to generate malware, ransomware, or other types of malicious code, which could then be deployed to compromise systems, steal sensitive data, or cause other forms of damage. The potential consequences include data breaches, financial losses, legal liabilities, and reputational damage.
Remediation
Investigate and improve the effectiveness of guardrails and other output security mechanisms to prevent the model from generating malware or assisting in malicious activities. Strengthen content filtering, restrict code generation capabilities, and develop contextual checks that flag requests for harmful actions.
Security Frameworks
A Prompt Injection Vulnerability occurs when user prompts alter the LLM's behavior or output in unintended ways. These inputs can affect the model even if they are imperceptible to humans, therefore prompt injections do not need to be human-visible/readable, as long as the content is parsed by the model.
Improper Output Handling refers specifically to insufficient validation, sanitization, and handling of the outputs generated by large language models before they are passed downstream to other components and systems. Since LLM-generated content can be controlled by prompt input, this behavior is similar to providing users indirect access to additional functionality.
An LLM-based system are often granted agency to e.g. call functions or interface with other systems via extensions. Agent-based systems will typically make repeated calls to an LLM using output from previous invocations to ground and direct subsequent invocations. Excessive Agency is the vulnerability that enables damaging actions to be performed in response to unexpected, ambiguous or manipulated outputs from an LLM.
Adversaries may abuse their access to a victim system and use its resources or capabilities to further their goals by causing harms external to that system. These harms could affect the organization (e.g. Financial Harm, Reputational Harm), its users (e.g. User Harm), or the general public (e.g. Societal Harm).
Reputational harm involves a degradation of public perception and trust in organizations. Examples of reputation-harming incidents include scandals or false impersonations.
Societal harms might generate harmful outcomes that reach either the general public or specific vulnerable groups such as the exposure of children to vulgar content.
User harms may encompass a variety of harm types including financial and reputational that are directed at or felt by individual victims of the attack rather than at the organization level.
An adversary may craft malicious prompts as inputs to an LLM that cause the LLM to act in unintended ways. These prompt injections are often designed to cause the model to ignore aspects of its original instructions and follow the adversary's instructions instead.
An adversary may inject prompts directly as a user of the LLM. This type of injection may be used by the adversary to gain a foothold in the system or to misuse the LLM itself, as for example to generate harmful content.
An adversary may inject prompts indirectly via separate data channel ingested by the LLM such as include text or multimedia pulled from databases or websites. These malicious prompts may be hidden or obfuscated from the user. This type of injection may be used by the adversary to gain a foothold in the system or to target an unwitting user of the system.
An adversary may use a carefully crafted LLM Prompt Injection designed to place LLM in a state in which it will freely respond to any user input, bypassing any controls, restrictions, or guardrails placed on the LLM. Once successfully jailbroken, the LLM can be used in unintended ways by the adversary.
Adversaries may abuse command and script interpreters to execute commands, scripts, or binaries. These interfaces and languages provide ways of interacting with computer systems and are a common feature across many different platforms. Most systems come with some built-in command-line interface and scripting capabilities, for example, macOS and Linux distributions include some flavor of Unix Shell while Windows installations include the Windows Command Shell and PowerShell.
AI system is evaluated regularly for safety risks - as identified in the MAP function. The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and can fail safely, particularly if made to operate beyond its knowledge limits. Safety metrics implicate system reliability and robustness, real-time monitoring, and response times for AI system failures.
AI system security and resilience - as identified in the MAP function - are evaluated and documented.
Mechanisms are in place and applied, responsibilities are assigned and understood to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use.
The organization shall define and document verification and validation measures for the AI system and specify criteria for their use.
The organization shall define and document the necessary elements for the ongoing operation of the AI system. At the minimum, this should include system and performance monitoring, repairs, updates and support.
The organization shall assess and document the potential impacts of AI systems to individuals or groups of individuals throughout the system's life cycle.
The organization shall assess and document the potential societal impacts of their AI systems throughout their life cycle.
The organization shall identify and document objectives to guide the responsible use of AI systems.
Attackers exploit code-generation features or embedded tool access to escalate actions into remote code execution (RCE), local misuse, or exploitation of internal systems.
Agents can misuse legitimate tools due to prompt injection, misalignment, or unsafe delegation - leading to data exfiltration, tool output manipulation or workflow hijacking.