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. Canum specializes in Language Model Security Testing—protecting your generative AI assets from exploitation, ensuring compliance, and building user trust.
Top 3 Industries Most at Risk Without Proper Language Model Security Testing
Vulnerabilities Closure Rate
Critical vulnerabilities Closure Rate
Threats to Language Model Systems
Prompt Injection Attacks
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.
Data Leakage via Model Outputs
If the LLM is trained on sensitive data, it may unintentionally regurgitate private or proprietary info in outputs.
Impersonation & Identity Spoofing
LLMs in conversational roles can be tricked into impersonating admins, leaking credentials, or enabling fraud.
Model Jailbreaking
Attackers try to bypass safety layers and generate toxic, misleading, or non-compliant outputs using adversarial inputs.
Tools & Frameworks We Use
Deliverables:
- Risk-based vulnerability report tailored for LLM systems.
- Prompt injection and output manipulation findings.
- Mitigation strategy for prompt sanitization, input filtering, and API segregation.
- Safety & compliance review for regulatory alignment (e.g., GDPR, ISO 42001).
Why Choose Canum 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.
→Our Security Testing Approach
- Testing Techniques: 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.
- Focus Areas: AI chatbots, voice assistants, and agents; LLM-integrated SaaS platforms; RAG (Retrieval Augmented Generation) pipelines; Fine-tuned/custom LLMs and orchestrated frameworks.
