Generative AI Risks & Cybersecurity: LLM Security

Explore the OWASP LLM top 10 vulnerabilities, including prompt injection, insecure output handling, training data poisoning, model denial of service, and supply chain risks.

Explore how nation-state and cybercriminal threat actors evolve with ai, using ai-powered reconnaissance, polymorphic phishing, deepfakes, and training data poisoning to target systems and supply chains.

Analyze real-world GenAI provider breaches through case studies to identify patterns like conversation history exposure and prompt injection, and apply an AI incident preparedness model for prevention, detection, and response.

Probe ai hallucinations by testing a fictitious researcher and paper to reveal fabrication, then compare with a real researcher to highlight verifiable facts and security risks.

Prompt injection is the SQL injection of the AI era, revealing no code-data boundary in LLM architectures. Attacker-controlled prompts can override instructions, creating a top vulnerability requiring emergency remediation.

Explore prompt injection techniques to extract a secret password from a model's system prompt, using direct attacks and parrot roleplay to bypass guardrails.

Explore indirect prompt injection in retrieval-augmented generation pipelines, where hidden instructions in documents and metadata can steer models, exfiltrate data, and enable varied attack paths across NoviAssist, Novacode, and Novacomply.

Explore indirect prompt injection and data exfiltration, including hidden instructions that override legitimate tasks, and learn defenses like input sanitization and output validation to prevent leakage.

Explore jailbreaking techniques that bypass safety refusals at the model level, including dual persona roles, roleplay, encoding bypasses, and multi-step prompt chaining, with actionable remediation.

Test three jailbreaking approaches—DAN, roleplay, and hypothetical framing—to assess how they bypass safety guardrails. Evaluate safety ratings and defense in depth.

Understand how training data poisoning corrupts models by embedding malicious behavior through data manipulation, backdoors, or label flips, and learn defense strategies across data provenance, anomaly detection, and canary testing.

Expose and mitigate model bias as a security risk by testing across three layers—training data, architecture, deployment—with paired demographic tests, intersectional analysis, and output distribution monitoring to address liability.

Explore how the bias detector lab tests name bias, gender bias, and nationality bias with paired prompts and compares outputs to reveal subtle fairness issues and ai security risks.

Explore how AI function calling expands the attack surface, enabling SSRF through prompt injection, tool use chain escalation, and URL manipulation, with real world NovaSys and MITRE Atlas insights.

Examine how token-based billing fuels AI denial-of-service through context window stuffing and 32,000-token responses, and deploy cost-aware defenses like token budgets, rate limiting, and circuit breakers.

Examine how AI hallucinations trigger risks by fabricating package names and citations, and apply a five-stage verification framework for supply chains with automated checks and human review to prevent harm.

Apply defense-in-depth with a five-layer AI defense stack, from input validation to governance, mapping threats to controls and aligning with the NIST AI Risk Management Framework and EU AI Act.

Examine how input validation defends AI systems at the user boundary with a three-tier pipeline—syntactic checks, semantic classification, and contextual analysis—to prevent prompt injection, noting latency 200 ms and limitations.

Guardrails builder trains defenders of AI chatbots by writing a defensive system prompt and enforces three principles—define allowed topics, hard deny rules, and a fallback response—tested against 10 prompt injections.

Learn how output filtering and validation defend users from breaches by detecting PII, malicious code, and harmful content through regex, ML classifiers, and tiered enforcement.

Implement output validation as a second gate that uses pi detection, code block checks, and url detection to block unsafe content before it reaches users.

Apply sandboxing and network isolation to an AI runtime, using a dedicated segment, strict function schemas, per-request data scoping, and ephemeral containers to contain breaches.

Develop rapid AI security detection by using AI-specific telemetry and monitoring across token usage, input validation, sandboxing, and conversation-level analysis to catch attacks within minutes and trigger swift incident response.

Apply secure coding practices for GenAI by enforcing data governance, secrets management, and least-privilege access, and by configuring secure defaults, token budgets, and content classification at boundaries.

Reframe the AI dev lifecycle with AI-specific security gates, integrating MITRE Atlas and OWASP LLM Top 10 to address prompt injection, data poisoning, and adversarial testing, from requirements to operations.

Explore building a three-category content classifier—safe, sensitive, malicious—using a local language model via Alima, testing against prompt injection and routing inputs in a secure pipeline.

Investigate how ai red teaming exposes adversarial vulnerabilities in llm defenses, testing system prompts, content classification, and jailbreaking to strengthen cybersecurity and risk management.

Generate a professional red team report for AI security, presenting executive summary, methodology, findings with severity, and prioritized remediation for leadership.

Track the evolving regulatory landscape across the EU AI Act, NIST AI RMF, and ISO-IEC 42001, highlighting high-risk obligations and governance requirements.

Map regulatory obligations to technical controls and documentation, building a formal risk management system and conformity evidence through five-layer defense, threat modeling, and ongoing monitoring.

Evaluate a system against the EU artificial intelligence act and classify it as high risk in employment; the lab maps articles and outlines required risk management and oversight.

Assess how SOC 2 Type 2 audits miss AI-specific risk surfaces like prompt injection, and how AI audits extend scope to training data provenance and model behavior for GenAI cybersecurity.

Generate a tailored AI policy for a financial services firm, outlining approved uses, data handling, security controls, and governance to meet EU AI Act, GDPR, and SOC 2.

Examine copyright, IP, and ethics in generative AI, highlighting training data provenance, licensing rights, open-source licenses, GDPR/CCPA implications, and governance controls.

Ai-generated phishing campaigns use polymorphic, personalized messages and deepfakes to outpace template defenses, while adaptive defense relies on threat intelligence and prompt-injection automation with continuous classifier retraining.

Explore agentic AI's autonomous tool use, multi-step workflows, and cross-agent delegation that extend attack surfaces. Defend with defense-in-depth: per tool permissions, tool profiles, parameter validation, memory monitoring, and drift detection.

Assess how quantum computing threatens TLS, model weights, and software signatures, and outline a phased migration to post-quantum standards (MLKEM, MLDSA, SLHDSA) with cryptographic inventory and hybrid key exchange.

Examine how AI risk, red teaming, and governance frameworks such as the EU AI Act, ISO 42001, and NIST AIRMF shape new cybersecurity roles, from prompt injection to adversarial testing.

Navigate a time-critical AI incident by crafting and evaluating an incident response plan for a prompt-injection breach in a customer-facing chatbot, applying NIST SP 800-61 and SANS methodologies.

Engage in a full red team exercise against a hardened ai system to test defense in depth, exploring prompt extraction, jailbreak, scope escape, and social engineering with actionable remediation.

Practice hands-on AI security with five-layer defense architectures and red team assessments, and translate governance and compliance across NIST AIRMF, EU AI Act, and ISO 42001.