GenAI Security: Risks and How to Protect LLMs

Generative AI reached production faster than any application class before it, and the chatbots, copilots, and agents teams now deliver real gains.

The same property that makes them useful, accepting untrusted text and acting on it, hands attackers an entry point that traditional application security cannot inspect. That problem lands on the developers and AppSec engineers who own the application layer, and it grows with every agent and integration shipped.

This guide covers GenAI security across the application lifecycle: how the threat environment shifted, the risk categories that define LLM security today (mapped to the OWASP LLM Top 10), and the controls that contain them.

Why GenAI Security Matters in 2026

Global cybercrime costs are projected to reach $23.84 trillion by 2027, and AI is pushing that number up from both sides. In 2025, attacks from AI-enabled adversaries rose 89 percent, and the average eCrime breakout time fell to 29 minutes, 65 percent faster than in 2024.

Malware now queries LLMs mid-execution to generate and obfuscate its own code, and the first reported AI-orchestrated cyber espionage campaign was disrupted in mid-September 2025. The applications you ship sit on the other side of that shift, a new attack surface your network perimeter never had to watch. Three incidents show how fast the failure modes escalated:

• Samsung ChatGPT data leak (2023): Engineers pasted source code, meeting notes, and equipment-test data into ChatGPT across three separate incidents, and proprietary data walked out through an inference endpoint.
  
• EchoLeak in Microsoft 365 Copilot (2025): Researchers had shown that instructions hidden in a web page or email could hijack an LLM agent that read them, and EchoLeak (CVE-2025-32711) turned that into a zero-click data exfiltration bug in production.
  
• Vercel supply chain breach (2026): An attacker rode the standing OAuth access of a compromised third-party AI tool into an employee’s Google Workspace and on into Vercel’s internal systems, reading API keys and database credentials without injecting a single prompt.
  

Three incidents, three entry points: an inference endpoint, a hidden instruction, a standing integration. None of them looks the same on the wire, and that variety is the problem the next section maps.

Where LLM Risk Lives Across the Lifecycle

AI helps defenders too, correlating telemetry to cut response times. The harder problem in 2026 is securing LLM applications themselves: the OWASP Top 10 names ten risks, and the table below maps each one to the lifecycle stage where it first enters an application.

OWASP risk (2025)	Where it enters	What it looks like in production
LLM01 Prompt Injection	Input handling	A model reads system instructions and attacker-supplied text as one stream and cannot tell them apart. EchoLeak, the zero-click Copilot bug, earned a CVSS score of 9.3.
LLM02 Sensitive Information Disclosure	Output handling	Private data flows back in a response. Samsung engineers pasted source code into a public chatbot, and shadow AI use roughly quadrupled in a year through personal accounts.
LLM03 Supply Chain	Build and dependencies	A compromised third-party model or library ships a backdoor into production. Third-party involvement in breaches doubled year over year to 30 percent.
LLM04 Data and Model Poisoning	Training and fine-tuning	A poisoned fine-tuning set or a tampered embedding index plants behavior that survives into the deployed model.
LLM05 Improper Output Handling	Output handling	Model output passes downstream without validation. The Air Canada chatbot’s invented refund policy, which a tribunal held the airline liable for, is the failure this covers.
LLM06 Excessive Agency	Agent actions	A model that can call functions or run code turns a small flaw into a large blast radius.
LLM07 System Prompt Leakage	Prompt handling	The system prompt reaches the user and exposes instructions, keys, or logic meant to stay hidden.
LLM08 Vector and Embedding Weaknesses	Retrieval (RAG)	Poisoned content in a knowledge base steers retrieval without changing a single model weight.
LLM09 Misinformation	Output handling	A confident wrong answer reaches a customer or triggers an automated workflow.
LLM10 Unbounded Consumption	Runtime	Unchecked inference drives runaway compute cost or denial of service.

Mapping each risk to a stage tells you where a control has to sit. The OWASP LLM Top 10 (version 2025) is the working taxonomy the table uses, and each of its categories ships with attack scenarios and mitigations you can prioritize by architecture.

For teams with compliance obligations, the NIST AI Risk Management Framework and ISO/IEC 42001 connect these technical risks to governance programs, and EU AI Act transparency rules for general-purpose AI have applied since August 2025.

Four Strategies That Protect LLM Applications

Four strategies cover the attack surface the sections above mapped: guardrails on the traffic in and out, continuous evaluation of what the model produces, discovery of the models and agents you run, and trace-level visibility into each request. Each catches failures the others miss.

1. Input and Output Guardrails

Guardrails sit inline with model traffic and check two points: the prompt before it reaches the model, and the response before it reaches the user. On the security side they block prompt injection, personally identifiable information (PII) leakage, SQL attacks, and schema violations; on the quality side they catch toxicity and off-topic responses while evaluators flag subtler issues like hallucination. A complete guardrail stack layers these checks across the full request lifecycle.

Screening every prompt and response in real time takes tooling that already sits in the traffic path. Coralogix is a full-stack observability platform that analyzes logs, metrics, traces, and security data in stream, and runs its AI Guardrails in that inline position. 

Each guardrail can block, rewrite, or flag a violating prompt or response based on the policy you set per application, covering injection attempts, PII exposure, and harmful content on both sides of the model call. Every enforcement decision is recorded, so a block can be traced back to the session that triggered it.

2. Continuous Evaluation of Model Behavior

Guardrails stop known-bad patterns at the edge. Catching drift, quality regressions, and brand-new failures takes continuous scoring of what the model returns, because these silent AI failures pass every infrastructure health check while degrading what users receive. Coralogix’s Evaluation Engine runs every prompt-response pair through evaluator models or custom rules, scoring for hallucination, toxicity, prompt injection, PII exposure, and data leakage as each response returns, then alerts with the full conversation trace when one trips. Guardrails enforce a hard line; evaluation tells you whether the model is behaving inside it.

3. AI Security Posture Management

The first blind spot is the one you cannot see: models and agents that teams stood up without telling anyone. Coralogix’s AI Security Posture Management (AI-SPM) cans your code repositories to catalog every model and agent and scores the security health of each agent. Discovery is its own job, separate from watching live traffic, which AI Monitoring and the Session Explorer handle.

4. Trace-Level LLM Observability

When something does go wrong, you need to see inside the request, not only its inputs and outputs. Coralogix’s LLM Tracekit instruments applications with OpenTelemetry (OTel), capturing the system prompt, user input, tool calls, retrieval steps, and model response as structured spans.

How to Implement and Automate GenAI Security

Picking the right controls is half the work. The other half is enforcing them without a human in the loop on every request, which is what turns a set of point controls into a repeatable program.

Use Cybersecurity Automation Tools

LLM-specific signals need to feed the same automation stack that already handles infrastructure threats. These seven tool categories form the building blocks of an automated GenAI security program:

• AI Guardrails: Keep each interaction inside defined safety and quality boundaries, blocking prompt injection, data leakage, and SQL abuse on the security side and toxicity or off-topic output on the quality side.
  
• Security information and event management (SIEM) tools: Aggregate and normalize events for analysis. In 2026 that includes prompt injection events, sensitive data leaving inference endpoints, and abnormal token consumption.
  
• Security orchestration, automation, and response (SOAR) tools: Chain detection and investigation into response through automated playbooks, so a flagged event triggers action without manual handoffs.
  
• Compliance automation platforms: Track obligations against regulations and standards. EU AI Act transparency rules for GPAI models began applying on 2 August 2025, and ISO/IEC 42001:2023 governs the management system around them.
  
• Vulnerability management tools: Scan hosts and prioritize remediation, now extended to repository and application scanning for unauthorized AI activity.
  
• Threat intelligence tools: Track the tactics, techniques, and procedures adversaries use, including how they are weaponizing AI.
  
• LLM observability: Give you trace-level visibility into prompts, retrievals, tool calls, and outputs, the only practical way to reconstruct what an agent did after an incident.
  

The point is to route these LLM-specific signals into workflows that already run, so enforcement stays continuous instead of manual, which is the core of secure AI operations. That routing lands in one of two places: in front of the model, or in the traffic around it.

Enforcement that waits for data to land in storage is already too late for a live prompt-injection attempt; Coralogix applies guardrail checks and evaluator scoring on one in-stream pipeline, so flagged events reach your SIEM and SOAR workflows already classified.

Apply Proven Automation Patterns

Two automation patterns recur across production deployments. Pre-inference controls check user input before it reaches the model: they swap PII for reversible tokens, or return a canned refusal when a prompt matches a known injection pattern, which stops the attack before you pay for inference. Usage-pattern analytics learn what normal traffic looks like and flag the deviations, which catches novel attacks that signature-based classifiers miss.

Define an AI Usage Policy

Controls work best against a written baseline. An AI usage policy names the approved models and tools, the data classes that must never enter a prompt, and the review path a team follows before shipping a new agent or integration. The Samsung leak happened in the absence of all three: engineers had no sanctioned alternative and no rule against pasting source code into a public chatbot. Pair the policy with repository scanning so violations surface as findings instead of incidents.

Enforce Least-Privilege Access for AI Integrations

Every third-party AI tool granted OAuth access to internal systems becomes part of your attack surface, as the Vercel breach demonstrated. Scope each grant to the narrowest set of resources the integration needs, set tokens to expire, and review standing access on a schedule instead of at onboarding only. The same discipline applies to your own agents: a model that can call functions inherits the permissions of the credentials behind it, so excessive agency is an access-control problem before it is a model problem.

Red-Team Your LLM Applications

Adversarial testing is the only way to learn how your application fails before an attacker does. The GenAI Red Teaming Guide structures that work across four areas: model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis. Run automated attack suites against every model or prompt change, supplement them with manual probing of agent tool calls, and feed each successful bypass back into your guardrail policies and evaluators. A red-team finding that never becomes a runtime control is a report, not a defense.

How Coralogix Secures LLM Applications

Coralogix runs guardrails, evaluation, and tracing on one in-stream pipeline, so they share the same runtime data instead of sitting in disconnected tools, while AI-SPM scans your repositories to keep the model and agent inventory current.

AI Guardrails block prompt injection and PII leakage at runtime, before an unsafe prompt reaches the model and before an unsafe response reaches the user, while the Evaluation Engine scores every response that gets through. AI-SPM keeps the inventory of models and agents current as teams ship, and LLM Tracekit with the Session Explorer turns any flagged session into a full trace with the complete conversation context.

That shared pipeline matters during an incident. A guardrail block, the evaluator score that preceded it, and the session trace that explains it all reference the same request, so the investigation starts with evidence instead of correlation work across tools.

If you want to see what real-time guardrails do to your own traffic, try Coralogix for free. The 14-day trial runs alongside your existing stack with no contract up front.

Frequently Asked Questions About GenAI Security

How is GenAI security different from traditional application security?

Traditional application security targets known vulnerability classes in deterministic code, like SQL injection and cross-site scripting. GenAI security adds non-deterministic model behavior, where the same input can produce different output across invocations, so you evaluate quality and safety on every request instead of validating code paths once at deploy time. The tooling differs too: guardrails and evaluation engines watch runtime model behavior, and session-level traces sit alongside standard request logs.

What does the OWASP LLM Top 10 cover that traditional OWASP doesn’t?

The Top 10 for Large Language Model Applications began in 2023, and this article centers on the 2025 version. It addresses risks specific to LLMs, such as prompt injection, information disclosure, data poisoning, excessive agency, and vector-related weaknesses. The traditional OWASP Top 10 still applies to the infrastructure hosting your LLM application, but it says nothing about the model behavior layer.

Can guardrails alone protect a production LLM application?

Guardrails are a required layer, not a complete defense. Classifiers trained on different data than the underlying model miss some injection techniques, and limited input size opens its own evasion paths. Continuous evaluation, posture management, and session-level observability cover what runtime guardrails leave open, and a free 14-day trial lets you run all four layers against your own traffic.

How do you monitor an LLM application you didn’t build in-house?

Third-party and API-based LLM applications get monitored at the integration boundary. You can instrument the API calls with OpenTelemetry to capture prompts, responses, latency, and token counts, and apply guardrails at the proxy layer between your application and the third-party model. AI-SPM helps you find every model and integration in your codebase, including the ones teams adopted without approval, which is where shadow AI tends to hide.