# From Cyber to LLMSec

As enterprises build AI-powered applications on top of large language models (LLMs), it’s critical to recognize that traditional cybersecurity frameworks no longer apply cleanly. The attack surface, threat landscape, and defense strategies are fundamentally different. Unlike classical systems with structured inputs and clear perimeters, LLMs introduce ambiguous interfaces, emergent behaviors, and novel risks like prompt injection, hallucinations, and training data leakage. These applications operate in a grey zone—where the threats are linguistic, probabilistic, and constantly evolving.

Below is a comparison table highlighting the operational differences between traditional cybersecurity and LLM/AI security (LLMSec). It outlines how attack vectors, monitoring strategies, and security controls diverge, and why enterprises need to adopt a new mindset when securing AI systems.

Category	Traditional Cyber Security	LLM Security (LLMSec)
Attack Payload	Binary code, scripts, SQL/XSS injections, file uploads	Natural language prompts, encoded text, embedded instructions
Attack Surface	Network ports, APIs, user input fields	Prompt interface, context window, RAG memory, embedded content
Input Validation	Format checks, schema validation, character escaping	Intent classification, adversarial prompt detection, semantic filtering
Output Risks	Malformed HTML/JS, command injection, data exfiltration	Hallucinated code, unsafe responses, training data leakage
Trust Boundary	Perimeter-based (firewalls, VPNs, RBAC)	Blurred boundaries—model can cross logic tiers and business rules
System Behavior	Deterministic; predictable from inputs	Probabilistic; behavior influenced by prompt phrasing, context, history
Detection Methods	Signature-based IDS/IPS, SIEM log correlation	Output classifiers, prompt pattern recognition, behavioral anomaly detection
Threat Vectors	Phishing, malware, lateral movement, privilege escalation	Prompt injection, jailbreaks, context poisoning, multi-turn attacks
Patch Model	Update firewall rules, software patches, signature updates	Retrain model weights, update prompt templates, adjust filters
Human Oversight	System administrators, SecOps teams	AI safety reviewers, red teamers, human-in-the-loop moderators
Monitoring Scope	Network traffic, system logs, access control logs	Prompt logs, output streams, conversation flow, classifier outputs
Incident Response	Quarantine endpoint, block IPs, patch affected systems	Roll back model version, patch prompts, update output filters, escalate
Attack Speed	Slower; requires exploit delivery or phishing success	Immediate; a single prompt can trigger unsafe behavior
Control Points	OS kernel, firewalls, IAM systems	Prompt pre/post-processing, context injectors, output filters, memory modules
Tooling	Firewalls (Palo Alto), SIEM (Splunk), EDR (CrowdStrike)	Moderation APIs, Prompt injection tools (e.g., Garak), embedding filters
Logging & Auditing	Access logs, network traces, OS logs	Prompt-response logs, output classifiers, memory access history
Supply Chain Risk	Dependency hijacking, CI/CD pipeline compromise	Hallucinated packages (slopsquatting), unverified generated code, RAG poisoning
Security Mindset	Infrastructure-focused, threat is external	Application-layer focused, threat can be user prompt or model behavior
Skill Set Needed	Network/cloud/infra security experts	Prompt engineers, AI red teamers, model safety experts, LangSec specialists

# Attack Surface & Threat Landscape

# Traditional Cybersecurity

In traditional enterprise systems, the attack surface is relatively well-defined. Interfaces are constrained—such as API endpoints, network ports, or file upload mechanisms—and expected input formats are structured and validated. Threats like SQL injection, cross-site scripting (XSS), buffer overflows, and malware are well-understood and have mature defenses, including input sanitization, firewalls, intrusion detection systems, and endpoint protection.

Security in this model typically focuses on protecting a known perimeter: securing ingress and egress points, authenticating users, and monitoring known traffic patterns. Vulnerabilities are often technical in nature, and mitigation strategies follow predictable, repeatable patterns grounded in decades of security best practices.

# LLMSec

LLM-powered applications break these assumptions. The primary interface is natural language—vastly more flexible, ambiguous, and user-controlled than traditional structured inputs. In this environment, every prompt a user submits becomes a potential attack vector, and it’s often difficult to draw a clear boundary between valid use and malicious manipulation.

Unlike classical systems, LLMs exhibit emergent behaviors. A harmless-looking prompt can lead to harmful output depending on context, phrasing, or even past interactions. Attackers exploit this unpredictability through techniques like prompt injection (embedding malicious instructions into user input), jailbreaks (bypassing safety constraints), hallucinations (confidently generating false or dangerous content), and data leakage (surfacing memorized training data).

These threats don’t fit neatly into the traditional security model. They operate at the level of intent, language, and behavior, requiring a fundamentally different approach to detection and mitigation—one that’s more probabilistic, adversarial, and dynamic in nature.

# Unique Challenges of AI Security

Securing AI systems—particularly those built on large language models—introduces challenges that traditional cybersecurity does not fully address. These systems behave probabilistically, adapt to context, and often function as black boxes. As a result, conventional assumptions about determinism, transparency, and threat modeling no longer hold. Below are three of the most critical, unique challenges in AI security.

# Black-Box Complexity

LLMs are inherently opaque. Their decisions emerge from billions of parameters, and even small input variations can lead to drastically different outputs.

Limited interpretability makes it difficult to understand why a model produced a specific result, complicating incident response and root-cause analysis.
Traditional debugging methods fall short when there’s no clear if-then logic to trace, and no logs of “decisions” in the classical sense.
As a result, identifying, reproducing, and fixing security failures is significantly harder than in traditional software systems.

# Data Poisoning & Model Extraction

Because LLMs are trained on vast, often opaque datasets, they are vulnerable to manipulation during both training and inference.

Data poisoning occurs when attackers introduce malicious content into the training set—intentionally skewing model behavior or embedding backdoors.
Membership inference attacks can reveal whether specific data was included in the training set, posing privacy risks for sensitive information.
Model extraction attacks allow adversaries to approximate or replicate proprietary models by querying them repeatedly, potentially stealing IP or identifying weaknesses.

# Rapidly Evolving Threats

The LLM threat landscape is moving faster than traditional defenses can adapt.

New attack techniques—such as prompt injection chains, typographic jailbreaks, or multimodal exploits—emerge regularly from the security community.
Cross-model transferability means a jailbreak or attack that works on one provider often works on others, accelerating the spread of threats.
Adversaries now include technically adept prompt engineers and automated systems capable of generating evolving attack patterns at scale.