AI is rapidly becoming the engine of enterprise innovation, driving efficiency and new capabilities across every sector. Yet, as organizations race to deploy large language models and intelligent agents, a critical question remains: Are these tools inherently secure, or can they be turned into sophisticated insider threats? The only way to securely accelerate your AI journey is to proactively test its capabilities. Security is not a barrier to adoption; it is the essential layer that enables your AI ecosystem to perform as intended.
The Shift from Academic Curiosity to Weaponized AI
The debate over whether AI can write malicious code is over. It can write malicious code. The current threat is less about a public model writing simple scripts. The bigger concern is now about the advanced "jailbreaking" of internal AI agents, models and applications integrated deep within your infrastructure that may be assisting developers with code commits or managing databases.
If an attacker can manipulate the model into bypassing its built-in guardrails, the agent stops being a helpful copilot. It becomes a machine-speed vector for attack. This risk is validated by industry research, including Anthropic’s “Disrupting AI Espionage” report, which details the concept of sabotage agents that are coerced into undermining systems through subtle, multiturn, hard-to-detect malicious actions.
Why “Only Generating” Malware Scripts Is Still Dangerous
Even without runtime execution capability, high-fidelity generated content matters due to its scalability, quality, evasiveness and access to insider pathways.
Scale
A model can produce many variants quickly with conceptual polymorphism, accelerating attacker iteration.
Quality
Modern LLMs can produce plausible, syntactically correct code templates or step lists that materially reduce the expertise needed for an attack.
Evasion
Variations and rephrasings defeat signature detectors and social-engineering filters that rely on static patterns.
Insider Pathways
A developer or automation pipeline that trusts generated artifacts can introduce the artifacts into builds, tests or repositories.
Put simply, if an agent can follow the descriptive steps generated by an LLM in the value chain and has any path, human or automated, to move content into a build or distribution pipeline, the generation step becomes the “enabler.”
Four Steps of Malware Script Generation
Here’s a simplified, high-level sketch of how a model could be guided to produce malware content to better understand the threat pattern and how these attacks happen.
1. Framing the Request
The prompt frames the task as legitimate (training exercise, lab or research), which can reduce guardrail triggers.
2. Breakdown
The model is asked to break the goal into small subtasks (behaviors to implement). Small, specific prompts are easier for models to answer.
3. Adding Associated Content
The model generates templates, code snippets or algorithms that correspond to each subtask (at a conceptual level).
4. Packaging All Instructions
The model describes, at a conceptual level, how these pieces would be assembled and tested.
We should think of these steps as conceptual building blocks. When combined, they lower the technical barrier for a human operator or downstream automation to build a working malicious artifact. Because this description omits any executable commands, code or specifics, it could get flagged as safe for AI defenses. Standard endpoint detection and response (EDR) solutions struggle to detect these unique, contextually generated exploits because they are built to look for known signatures.
Prisma AIRS AI Red Teaming Tests Your AI for Malware Generation
You cannot secure what you cannot see. The goal of Prisma® AIRS™ AI Red Teaming is to give CISOs clear, evidence-based insight into their internal models’ real capabilities and their AI systems’ potential to be misused by threat actors. It is purpose-built to automate the adversarial testing required to uncover these sophisticated flaws.
Automated Malware Generation Testing
Our methodology uses specialized “malware generation” test categories. We do not just look for simple code; we push the model to its limits, simulating role-playing attacks to trigger the generation of complex outputs, including shellcode loader frameworks and scripts designed for data exfiltration. This provides statistically significant evidence of an AI model’s potential for weaponization.
Testing for Polymorphic Capabilities
Prisma AIRS AI Red Teaming is built on the principle that AI models are probabilistic in nature. It iterates its attacks to see if your model can be forced to rewrite malicious code in different ways to evade detection. This stress-tests your AI application's or model’s robustness against the type of polymorphic attacks that bypass standard EDR.
An example of a Prisma AIRS AI Red Teaming report that proved a custom application’s propensity to generate malware code using a certain attack technique.
With the right testing discipline within Prisma AIRS AI Red Teaming, you can move away from guessing whether your systems are safe to knowing if they are.
Prisma AIRS AI Red Teaming as Your New Deployment Gate
The power of AI is undeniable, and every team within every organization should feel empowered to leverage it. However, this power must be approached with robust security protocols. We must ensure that our most powerful new tools cannot be weaponized against us.
AI Red Teaming is the essential first step — it acts as a high-fidelity microscope, giving you the necessary ability to look deep into your model behavior flaws that allow for such harmful manipulation. Once you establish this foundation of awareness and security validation with Prisma AIRS AI Red Teaming, you can confidently leverage the power of the Prisma AIRS platform and secure your AI systems. Secure AI adoption begins with measuring your risk.