PRISMStrike: Red Teaming for LLMs
Test your language models against adversarial attacks, prompt injections,and safety bypasses—before they hit production.
the challenge
As LLMs move from experimentation to production, traditional testing methods are not designed to uncover how they behave under hostile inputs, leaving critical risks unaddressed.
Vulnerability to prompt injection and jailbreak attack
Models can be manipulated to bypass safeguards and generate unintended or harmful outputs.
Unpredictable behavior under adversarial inputs
LLMs may respond inconsistently or unsafely when exposed to malicious or edge-case prompts.
Risk of sensitive data leakage and unsafe responses
Models may inadvertently expose confidential information or produce non-compliant outputs.
Ineffective or bypassable safety guardrails
Controls that work in standard scenarios may fail under targeted adversarial testing.
Limited assurance before production deployment
Organizations often lack confidence in how models will perform under real-world misuse conditions.
Our Approach
PrismStrike simulates real-world threats like jailbreaks, bias exploits, and prompt injections. Based on the OWASP AI Testing Guide, it validates your model's safety posture.
Maliciously Fine-Tuned LLM
Uses SISA's internally developed adversarial LLM to generate sophisticated test cases.
Intelligent Attack Vectors
Converts intent + model behavior + known guardrails into targeted attack vectors.
Human-in-the-Loop
LLM red team experts review and iterate on test results for real-world reliability.
White-Box Testing
Specify your deployed guardrails to generate targeted test cases that attempt to bypass them.
Service Offerings
PrismStrike focuses on testing how LLMs behave under hostile conditions, exposing weaknesses, and strengthening model resilience against real-world misuse.
Upload the model: Users upload their model, provide a Hugging Face repo, or define an inference API endpoint.
Define Domain & App: Specify domain (e.g. healthcare, finance) or app type (chatbot, co-pilot) to tailor the red team logic.
Launch Known Attacks: PrismStrike tests the model with its pre-built test case library across multiple vulnerability types.
Generate New Test Cases: Our red team LLM creates fresh, domain-specific test cases dynamically to discover edge failures.
Adaptive Evaluation: Responses are analyzed by PrismStrike Intelligence to detect weaknesses and adapt testing accordingly.
BENEFITS
By testing models against real adversarial scenarios, PrismStrike helps organizations strengthen resilience while maintaining performance and efficiency.
Cost efficiency:
Optimize resource usage with up to 70% lower GPU memory consumption during testing and validation.
Faster remediation
Reduce time to fix vulnerabilities with up to 80% quicker turnaround on identified issues.
Accelerated deployment with higher assurance:
Enable faster go-to-production with higher assurance in model security and behavior.
Retention of model fluency:
Maintain model reasoning capabilities and output quality while strengthening security controls.
WHY SISA
Our approach provides measurable outcomes and regulatory alignment to help organizations confidently assess and benchmark AI model security.
Validated attack scenarios with documented evidence
Execute controlled adversarial tests with clear proof of how models respond under real attack conditions.
Clear pass/fail outcomes against structured test cases
Evaluate model behavior against defined test scenarios to provide objective, defensible results.
Baseline attack resilience metrics for benchmarking
Establish measurable benchmarks to track and improve model resilience over time.
Regulatory alignment with leading AI frameworks
Map results to standards such as ISO/IEC 42001, EU AI Act (Article 40), and HITRUST AI Security Assessment.
Secure Your AI Deployments