Facing a Cyber Threat?
Language Model Security Testing
Specializes in Language Model Security Testing protecting your generative AI assets from exploitation, ensuring compliance, and building user trust.
As AI systems powered by Large Language Models (LLMs) become integrated into business operations, they also introduce new security risks. LLMs can be manipulated via prompt injections, data leakage, or even used as a gateway to backend systems. Cyber Shakthi specializes in Language Model Security Testing—protecting your generative AI assets from exploitation, ensuring compliance, and building user trust.
Threats to Language Model Systems
- Prompt Injection Attacks
- Data Leakage via Model Outputs
- Unauthorized Access to Plugins or APIs
- Impersonation & Identity Spoofing
- Model Jailbreaking
Malicious users manipulate the LLM by embedding hidden instructions that override intended prompts, causing it to leak sensitive system info, perform unintended actions, or generate harmful content.
If the LLM is trained on sensitive data, it may unintentionally regurgitate private or proprietary info in outputs.
LLMs in conversational roles can be tricked into impersonating admins, leaking credentials, or enabling fraud.
Attackers try to bypass safety layers and generate toxic, misleading, or non-compliant outputs using adversarial inputs.
Tools and Techniques we Use
Deliverables
Our Security Testing Approach
- Prompt injection & context poisoning
- Adversarial prompt crafting
- Training data leakage testing
- Permission escalation and plugin abuse simulation
- Bias and toxicity detection audits
- Session manipulation testing in chat-based models.
If the LLM is trained on sensitive data, it may unintentionally regurgitate private or proprietary info in outputs.
Why Choose Cyber Shakthi for LLM Security?
Dedicated AI security team trained on GenAI threats.
Deep testing for OpenAI, Anthropic, Cohere, Meta, and open-source LLMs.
Custom test cases based on your industry and usage.
Developer + compliance-friendly reports.
Optional collaboration during model fine-tuning.
