Secure Your
Intelligence
As AI models become the new attack surface, traditional security isn't enough. We stress-test your LLMs against prompt injection, data poisoning, and model theft.
AI Security Mitigation
Our comprehensive defense strategies protect your AI systems across all attack vectors. Hover to explore each mitigation approach.
Input Validation
Implement rigorous input sanitization and context-aware filtering to prevent prompt injection attacks
Solution: Multi-layer validation, adversarial training, and real-time monitoring
Tech: We deploy regex-based sanitization layers combined with semantic analysis models (e.g., BERT) to detect and block adversarial prompts before they reach the LLM context window.
Data Integrity
Cryptographic provenance tracking and statistical analysis to ensure training data integrity
Solution: Blockchain verification, anomaly detection, and secure data pipelines
Tech: Utilizing Merkle trees for dataset versioning ensures that any unauthorized modification to the training corpus is immediately flagged, preventing data poisoning attacks.
Robust Defense
Adversarial training and defensive distillation to harden models against crafted inputs
Solution: Ensemble methods, input preprocessing, and certified defenses
Tech: We implement randomized smoothing and defensive distillation during the fine-tuning phase to increase the model's robustness against gradient-based adversarial examples.
API Protection
Rate limiting, output watermarking, and query monitoring to prevent model extraction
Solution: Differential privacy, query budgets, and behavioral analysis
Tech: API gateways are configured with strict rate limits and anomaly detection rules that trigger CAPTCHAs or blocks when query patterns resemble model extraction attacks.
Privacy Preservation
Differential privacy techniques and secure aggregation to protect training data
Solution: Federated learning, homomorphic encryption, and noise injection
Tech: By injecting calibrated noise into the gradients during training (DP-SGD), we mathematically guarantee that individual data points cannot be reconstructed from the model parameters.
Safety Guardrails
Multi-layer content filtering and behavioral constraints to prevent jailbreaking
Solution: Constitutional AI, RLHF, and dynamic safety boundaries
Tech: We use a secondary 'Constitution Model' to evaluate every output against a set of safety principles in real-time, rewriting or blocking harmful responses before they are returned to the user.
AI Threat Landscape
The AI security landscape is evolving rapidly. Understanding these threats is the first step in building resilient, secure AI systems.
Prompt Injection
Attackers manipulate LLM inputs to override system instructions, bypass safety guardrails, or extract sensitive information.
"In 2023, researchers bypassed Bing Chat's safety filters using 'DAN' (Do Anything Now) prompts, forcing it to generate hate speech and reveal internal rules."
Data Poisoning
Malicious actors inject corrupted data into training sets to manipulate model behavior or introduce backdoors.
"A study showed that poisoning just 0.01% of a dataset could introduce a hidden backdoor, causing a facial recognition model to misidentify specific targets."
Model Extraction
Adversaries use API queries to reverse-engineer proprietary models, stealing intellectual property.
"Researchers successfully replicated a commercial language model's functionality for less than $500 by querying its public API and training a student model on the outputs."
Membership Inference
Attackers determine if specific data was used in model training, potentially exposing sensitive information.
"Attackers were able to extract full credit card numbers and SSNs from a language model by prompting it with prefixes of sensitive data sequences."
Jailbreaking
Sophisticated techniques to bypass content filters and safety mechanisms through crafted prompts.
"The 'Grandmother Exploit' tricked models into revealing bomb-making instructions by asking them to roleplay as a deceased grandmother who used to work in a chemical factory."
Adversarial Attacks
Carefully crafted inputs that appear benign but cause models to produce incorrect outputs.
"Adding imperceptible noise to an image caused a Tesla autopilot system to misclassify a Stop sign as a 45 MPH speed limit sign."
Many of these threats also apply to traditional web applications. Ensure your APIs and front-end interfaces are secure.
Review Application SecurityAI Security Lifecycle
End-to-end protection for your AI models, from training to inference.
Assess
Deep analysis of model architecture, training data, and deployment environment.
Red Team
Adversarial simulation using proprietary datasets to trigger jailbreaks and hallucinations.
Harden
Implementation of guardrails, input sanitization, and output filtering.
Monitor
Continuous real-time detection of drift, bias, and new attack vectors.
Thinking Like the Enemy
Standard testing isn't enough for AI. Our AI Red Teaming service employs human creativity to find the cracks in your model's logic. We simulate sophisticated attacks to uncover biases, hallucinations, and security flaws before they go live.
Infrastructure Security
AI models don't exist in a vacuum. We also test the underlying infrastructure hosting your models.
Explore Network PentestingCommon Attack Vectors
Prompt Injection
Manipulating inputs to override system instructions.
Jailbreaking
Bypassing safety filters to generate harmful content.
Model Inversion
Reconstructing training data from model outputs.
Supply Chain Attacks
Compromising third-party libraries or base models.
Red Teaming Process
Reconnaissance
Mapping model architecture and inputs.
Probing
Testing boundaries and safety filters.
Exploitation
Executing complex attack chains.
Reporting
Detailed findings and remediation.
Case Studies
See how we've helped organizations across industries secure their AI deployments against sophisticated attacks.
FinTech Chatbot Jailbreak
The Challenge
A leading fintech company deployed an LLM-powered customer support agent. They needed to ensure it wouldn't provide financial advice or reveal sensitive user data.
The Attack
Our Red Team used 'role-playing' prompt injection techniques to bypass the model's safety instructions. We convinced the bot it was a 'Financial Advisor Mode' which allowed it to recommend high-risk crypto investments, violating compliance regulations.
The Fix
We implemented a secondary 'Constitutional AI' layer that evaluates every response against a set of strict financial compliance rules before sending it to the user.
Healthcare PII Extraction
The Challenge
A hospital system used an internal LLM to summarize patient records. They needed to verify that the model wouldn't leak PII to unauthorized staff.
The Attack
Using 'Model Inversion' attacks, we queried the model with specific prefixes found in medical records. The model completed the sequences, revealing real patient names, diagnoses, and SSNs that it had inadvertently memorized during fine-tuning.
The Fix
We recommended implementing Differential Privacy (DP-SGD) during the fine-tuning process and strict output filtering to redact PII patterns.
SaaS Code Assistant Backdoor
The Challenge
A SaaS provider built a coding assistant for their developers. They worried about supply chain attacks on the base model.
The Attack
We discovered that the open-source model they fine-tuned had a 'poisoned' dataset. By triggering a specific rare keyword, the model would generate vulnerable code (SQL injection flaws) instead of secure code.
The Fix
We helped them switch to a verified, cryptographically signed base model and implemented a 'Red Team' evaluation step in their CI/CD pipeline for model updates.
Secure Your AI Future
Don't let security be the bottleneck for your AI innovation. Our expert team provides the assurance you need to deploy with confidence.
Our Security Toolkit
We leverage industry-standard tools alongside our proprietary engines to deliver comprehensive AI security assessments.
Regulatory Compliance
Ensuring your AI deployments meet global safety and ethical standards
EU AI Act
Comprehensive compliance auditing for high-risk AI systems under the new EU regulations.
NIST AI RMF
Alignment with the NIST AI Risk Management Framework for trustworthy and safe AI.
ISO 42001
Certification readiness for the global standard on Artificial Intelligence Management Systems.
OWASP LLM Top 10
Testing against the critical security risks identified by the OWASP foundation for LLMs.
Why Trust RadiumFox?
We are at the bleeding edge of AI security, defining the standards that others follow.
Pioneering Research
We don't just use tools; we build them. Our team actively contributes to the discovery of new zero-day vulnerabilities in major LLMs.
Hybrid Intelligence
Automated fuzzing finds the cracks, but our elite human red teamers find the logic flaws that machines miss.
Proprietary Datasets
We test your models against our massive, internal library of custom jailbreaks, prompt injections, and adversarial examples.
See It In Action
Test our security guardrails against a simulated attack.
AI Security FAQ
Answers to your most pressing questions about securing Artificial Intelligence.
Join Us. Cut Costs.
Focus on What Matters.
Unlock high-impact penetration testing that drives real security gains. Led by experts, tailored for results, and designed to stay budget-friendly.
Submit Info
Share your environment, scope, or compliance needs via our quick form.
Senior Review
A lead RadiumFox engineer reviews and tailors your assessment—no junior handoffs.
Optional Scoping Call
We'll clarify priorities and technical details if needed.
Clear Quote
Expect a fixed-cost proposal—no hidden fees or fluff.
Fast Kickoff
Once approved, most projects launch within 5–7 business days with full support.