When Good GPTs Go Bad: How Trusted AI Tools Are Exploited for Attacks

Artificial intelligence is changing everything—fast. What once felt like science fiction is now part of daily life, unlocking new efficiencies and driving rapid innovation. At the heart of this revolution are large language models (LLMs), and particularly generative pre-trained transformers (GPTs), which have redefined what AI can accomplish.

GPTs represent one of the most powerful advancements in AI, enabling machines to generate human-like text with surprising fluency. What started as a theoretical concept is now everywhere—helping people draft emails, write code, summarize reports, and assist in real-time decision-making.

Unfortunately, the same qualities that make GPTs valuable—their accessibility, adaptability, and ability to augment human potential—also make them easily weaponized by attackers. From social engineering scams to automated malware generation, GPTs can be exploited to launch more convincing, scalable, and efficient attacks with minimal effort.

What we’re seeing now is the rise of malicious AI: the utilization of artificial intelligence to deceive, defraud, and attack.

To fully understand the risks posed by malicious AI, we need to explore how mainstream AI tools are being exploited, the real-world consequences of AI-driven attacks, and why this moment represents a turning point for cyber defense.

GPT models are sophisticated language processors trained on vast datasets to generate coherent, contextually relevant text. However, their design—which prioritizes adaptability and response generation based on learned patterns rather than a true understanding of intent or context—makes them inherently vulnerable to manipulation.

Flaws in training data, model alignment issues, and susceptibility to adversarial inputs can be exploited via cleverly disguised prompts designed to override safeguards and generate harmful content. The complexity of human language and the subtlety of malicious injections further complicate the development of robust safeguards that can block these attempts without overly restricting the model’s creative or productive outputs.

But how exactly do attackers turn these weaknesses into opportunities? Here are a few examples.

Data Poisoning

Data poisoning is a stealthy yet powerful attack tactic in which adversaries manipulate a model’s training data to alter its behavior. By injecting biased, misleading, or malicious inputs into the dataset, attackers can influence a GPT’s outputs, causing it to generate false information, reinforce dangerous narratives, or weaken its safeguards.

Poisoned data can be subtly embedded in publicly available sources or inserted during fine-tuning, making detection difficult. Once compromised, a model may unknowingly assist in fraud, misinformation campaigns, or automated cyberattacks.

Jailbreak Techniques

Jailbreaking a GPT involves circumventing its built-in safety mechanisms to produce restricted or harmful content. Attackers use carefully crafted prompts, encoded instructions, or multi-step exploits to sidestep ethical constraints and trick the model into providing prohibited responses.

Some techniques involve role-playing scenarios, adversarial commands, or breaking requests into smaller, less detectable steps. Once successful, jailbreaks can facilitate the generation of misinformation, illicit code, or even fraud-enabling guidance.

Prompt Injection and Model Reprogramming

Prompt injection manipulates a GPT’s inputs to override its intended behavior, often leading the model to ignore safeguards or execute unauthorized actions. Attackers compose deceptive prompts that confuse the system, making it generate harmful content, leak sensitive information, or bypass ethical constraints.

More advanced model reprogramming techniques go further, embedding persistent instructions that subtly alter responses across multiple interactions. These attacks enable threat actors to redirect outputs, automate social engineering, or create persistent backdoors in AI-driven systems.

See how we were able to impersonate threat actors and bypass safeguards on leading AI models. Download the white paper →

AI has become an indispensable tool for work and creativity, but cybercriminals see it as something else entirely—a shortcut to deception, fraud, and automation of malicious campaigns.

One of the most immediate concerns related to the rise of malicious AI is the potential for data breaches. Rather than taking days to craft the perfect message to trick an unsuspecting target, attackers can use carefully crafted prompts or LLM-assisted interactions to socially engineer end users in minutes. The result could be disastrous, as targets are tricked into disclosing sensitive information—exposing confidential data and placing organizations at risk of regulatory violations and reputational harm.

Financial fraud and social engineering are also evolving at an alarming speed. AI tools enable cybercriminals to craft highly convincing phishing emails, fraudulent communications, and deepfake impersonations with minimal effort. The result is an increasingly complex fraud landscape where attackers can rapidly scale operations and target victims who may never suspect they’re being deceived.

Beyond financial losses, malicious AI can cause lasting reputational harm. Organizations that fall victim to these attacks often face prolonged scrutiny, with customers, investors, and regulators questioning their ability to protect sensitive data and maintain secure operations. In fact, the erosion of trust can often be far more damaging than the immediate impact of a single incident.

In interconnected systems, the risks escalate even further. In environments where AI is embedded into systems or automation workflows, a single malicious prompt can lead to unintended actions—such as executing harmful code or exposing data—with the potential to disrupt operations, corrupt data, or compromise entire supply chains.

As AI tools become embedded in critical systems, the consequences of exploitation become more severe—and more difficult to contain.

See examples of malicious GPTs that were purpose-built for cybercrime. Download the white paper →

The threat of malicious AI isn’t theoretical—it’s here, and it’s already changing the attack landscape. The question is no longer if you’ll be targeted, but how prepared you’ll be.

But while there is no denying that malicious AI is rewriting the rules of cybersecurity, it hasn’t taken control out of organizations’ hands. Even with attackers using generative models to scale deception and bypass traditional defenses, security leaders still have the power to outpace them with the right strategy.

That said, defending against malicious AI requires more than reactive measures; it demands forward-thinking investments, continuous education, and defensive AI protection that evolves alongside the threats. The barriers to launching sophisticated attacks are rapidly disappearing, but with an ongoing commitment to awareness, innovation, and vigilance, organizations can stay ahead of this accelerating threat.

Download our white paper for real-world examples of AI exploits, predictions for the future of malicious AI, and actionable strategies to defend against malicious AI.