The incredible capabilities of large language models (LLMs) like ChatGPT make them irresistible to developers seeking to create next-generation apps. But this frenzied adoption risks exposing users to new dangers. LLMs have unique vulnerabilities that most developers don’t grasp. Building secure applications requires understanding these novel risks and implementing tailored safeguards.
Top Threats
Prompt injection attacks top the list of concerns. Attackers can carefully craft malicious prompts that trick the LLM into leaking sensitive data, executing unauthorized code, or taking other harmful actions. Prompt injections exploit the fact that LLMs interpret all text as user input. They can occur directly through a compromised chatbot or indirectly via text from websites and other external sources.
Another serious risk is training data poisoning. Adversaries can manipulate the data used to train LLMs to introduce dangerous biases, security holes, or unethical behaviors. For example, a competitor could poison data to favor their own brand. The consequences surface when real users interact with the corrupted LLM.
Supply chain vulnerabilities are also likely given LLMs’ reliance on diverse components like datasets, pre-trained models, and plugins. Any of these could contain vulnerabilities allowing exploits like remote code execution or privilege escalation. Malicious plugins pose a particular threat due to their unchecked access to LLMs.
Over-reliance on unreliable LLM outputs poses big hazards too. LLMs can generate logical, persuasive responses even when totally incorrect, leading to harmful misinformation or faulty decision-making if not validated. Insecure code generated by LLMs risks introducing vulnerabilities into applications as well.
Finally, model theft has serious competitive and financial implications. Attackers who copy proprietary LLM models gain intellectual property and sensitive data while eroding the model owner’s advantages.
New & Old Vulnerabilities Collide
Many LLM vulnerabilities resemble traditional software security issues like code injection or supply chain exploits. However, factors like LLMs’ use of natural language and deep neural networks create new nuances. For example, while SQL injection has long plagued applications, the way prompt injection attacks manipulate neural network behavior represents a wholly new challenge.
Other LLM vulnerabilities have no prior software equivalent. Training data poisoning does not map to any non-ML vulnerability. And while insider data theft is not new, the theft of ML models themselves is an emerging danger.
In some cases, old and new intersect – an insecure plugin vulnerability could enable a novel prompt injection. Developers must broaden their scope to secure the unique LLM attack surface.
Layered Defenses Keep Applications Secure
Fortunately, protections exist to mitigate these varied risks. To prevent prompt injection, input validation, sanitization, and least privilege access controls are crucial. Maintaining user control over LLM actions also limits unauthorized behaviors. Code libraries such as Nvidia’s NeMo Guardrails can even filter user input at the prompt level.
For training data, carefully vet sources, sanitize inputs, and use techniques like federated learning to detect poisoning. Follow MLOps best practices for secure models. Limit the functionality and autonomy granted to LLMs based on necessity. Rigorously validate outputs using consistency checks and human review. Warn users about potential inaccuracies.
For model security, employ strong access controls, monitor activity, and implement adversarial training to harden models. MLOps platforms with model versioning aid provenance and auditing as well.
A Responsible Balancing Act
The power of LLMs entices developers to rapidly deploy inventive applications. But carelessness now can lead to compromised security for years to come. Taking time upfront to implement layered protections against emerging LLM-specific vulnerabilities will let developers harness these models safely and responsibly.
