AI Security Services

Prompt Injection: Prompt injection is an attack type aimed at manipulating an AI system’s behavior, reasoning, or application-level logic by embedding malicious instructions into the prompt and other input sources.

Jailbreaking: Jailbreaking is an intentional attempt to sidestep an AI system’s guardrails and manipulate it to generate outputs or perform actions that its safeguards originally prevent.

Intellectual Property (IP) Infringement: AI models with access to external data sources may ingest and use protected data in their operations or responses, triggering legal repercussions. 

IP Theft: Malicious actors can prompt and use AI models with the intent to replicate their proprietary strengths and cause them to regenerate proprietary data they were trained on, leaking enterprises’ IP.

Model Drift: Models can drift over time as operational data deviates from training data, leading to performance degradation, making the model easier to manipulate.

Hallucinations:  Hallucinating AI models can generate incorrect outputs that may lead to ill-informed decision-making or disastrous automated activities.

Data Poisoning: This is an adversarial attack where the attacker intentionally manipulates retraining data to cause models to generate wrong results, undermining its reliability, precision, and efficiency.

Training Data Exposure:  If appropriate privacy masking, federation, and other anonymization controls are not applied to training data, models can ingest and reproduce private information.

Insecure Data: Complex AI systems have an increased data surface, and similar levels of control cannot be exerted over all external sources. Any vulnerability in data pipelines or databases that they access may be reflected in the model’s outputs. 

Compliance Risks: Sensitive data leakage, broken lineage, and privacy-violating inferences are just some of the many compliance risks caused by improper data handling and insufficient guardrails in AI systems.

Unsafe AI Use: Despite guardrails, AI models may inadvertently generate wrong outputs or trigger wrong actions if they have agentic capabilities, making it unsafe for users.

Inappropriate Access Control: Without strong identity and access management (IAM), the chances of malicious actors tweaking the model or users mistakenly harming the model and its parameters increase significantly.

AI-Accelerated Attacks: AI-augmented cyberattacks are outpacing traditional cybersecurity tools, and certain AI-accelerated deepfakes, LLM-generated malicious prompts, and other attacks can also bypass AI-based security.

Insecure Output Handling: If an AI system’s outputs are not sanitized and validated from a security lens, they can be exploited for downstream vulnerabilities like cross-site scripting (XSS), Server-Side Request Forgery (SSRF), or SQL Injection based on how other systems leverage their output.

Model Denial of Service (DoS): Malicious actors can overwhelm an AI model with prompts and requests that maximize compute usage, making it unavailable for legitimate users or significantly undermining its performance. This attack also spikes compute costs, making the AI system a financial burden and lowering its ROI.

Supply-Chain Risks: Complex AI systems may include several external dependencies, including foundational and free models, agentic AI frameworks, and AI tools, all of which contribute to their overall vulnerability profile.

Third-Party Dependencies: Model and third-party dependencies increase not just security risks propagating downwards, but also behavior and output risks contaminating your AI’s outputs and actions.

Infrastructure Vulnerabilities: APIs and other middleware elements can be dangerous if misconfigured, especially when AI deployments interact with legacy systems.