Large language models (LLMs) have taken center stage since the introduction of ChatGPT in November 2022. They have reconfigured the business landscape and introduced operational efficiencies across industries, from optimizing supply chains to providing personalized customer experiences. These models are quickly becoming ubiquitous and their potential seems close to infinite. However, one trait they share with earlier technologies is that their promise is fraught with challenges that have made new and noteworthy modifications to the enterprise landscape.
While most business leaders know about the vulnerabilities and risks inherent in traditional IT systems—such as software bugs, data breaches, or phishing attacks—LLMs face a significantly different array of risks that are rapidly expanding the nature of cybersecurity risk. Corporate decision-makers have already passed the point at which they could choose to spend time learning about these threats, and are well into the phase in which these unprecedented capabilities require protection to be deployed.
The larger and more complex the organization’s LLM infrastructure becomes, the greater the challenge is to ensure that each is secure from these new types of threats, which we detail below:
- Jailbreaks or prompt injection attacks: These attacks involve a user crafting a query that includes instructions or “rules” that direct the model’s behavior with the intention of overriding internal controls that prevent the model from executing bad commands.
- Data poisoning: This involves subtly manipulating the training data so the model, once trained, behaves in unintended ways—essentially compromising its future predictions or decisions.
- Adversarial attacks: These typically involve careful scrutiny of model outputs to deduce information about the model or its parameters, its training data, or sensitive data included in other datasets.
The operational and financial implications of these vulnerabilities and threats range from bad to catastrophic, depending on the severity of the attack and the nature of the organization’s core business operations. Given the strategic importance of AI in the enterprise today, it is crucial to understand the differences between the types of threats that exist in the traditional IT security arena and those in the AI security domain. Undertaking to make the large investment of resources necessary to bring AI to scale in an organization without a corresponding commitment to understanding and mitigating its risks can be a costly oversight.
CalypsoAI’s ground-breaking LLM security and enablement solution solves for the most pressing AI security concerns organizations face when deciding to deploy multiple LLMs, including private models, across their enterprise. Our solution targets prompt injection attacks by rigorously scanning all prompts for semantic patterns and categories, such as role-playing, reverse psychology, hypothetical situations, or world-building, which indicate a user is instructing the LLM to ignore or override system controls. Administrators can set the sensitivity of the Toxicity, Banned Terms, and other scanners to identify or block content misaligned with the organization’s acceptable use policy and values. User engagements can be tracked and audited to gain insight into cost, usage, and other concerns.
Model-agnostic, with built-in multi-model, multimodal capabilities and policy-based access controls (PBAC) for groups and individuals, our solution provides wraparound protection for LLMs while boosting productivity and without introducing latency.
The potential of AI systems is undeniably transformative, and with great power comes great responsibility. As these systems become more embedded in business processes, the unique vulnerabilities they introduce need the same—if not more—attention as traditional IT risks. Ensuring your organization is protected from new threats is not about mastering the intricacies of AI, but about recognizing the existence of the vulnerabilities and taking the necessary steps to safeguard your AI-powered future.
Security Risks: Traditional InfoSec vs AISec
Aspect |
Traditional Information Security Risks |
AI Security Risks |
| Nature | Concerns around data, systems, and networks | Concerns around the misuse or manipulation of AI models and data |
| Typical Threats | Phishing, malware, DDoS attacks, unauthorized access | Adversarial attacks, model inversion, data poisoning |
| Attack Vector | Exploiting software/hardware vulnerabilities, human error, or system misconfigurations | Manipulating input data, exploiting model behavior, corrupting training data |
| Primary Objective | Data theft, system disruption, unauthorized access | Model manipulation, knowledge extraction, degraded performance |
| Risk Drivers | Outdated systems, weak passwords, unpatched software | Insufficient model robustness, over-reliance on AI decisions, lack of interpretability |
| Mitigation Strategies | Firewalls, patches, MFA, encryption | Robust model training, input validation, model monitoring |
| Impact of Breach | Data loss, financial implications, reputational damage | Erroneous AI decisions, loss of trust in AI systems, model retraining costs |
| Unique Concerns | Physical access, insider threats, third-party vendors | Transfer attacks (attack strategies that transfer from one model to another), model stealing, hyperparameter tuning attacks |
| Evolution Pace | Constantly evolving with technological advancements | Rapidly growing with advancements in AI and machine learning research |
| Main Targets | Databases, networks, applications, user accounts | Pre-trained models, AI APIs, training datasets, AI inferences |
