What are LLM security tools?
LLM security tools are specialized systems that protect large language models (LLMs) across their entire lifecycle: development, deployment, and ongoing operation. Their purpose is to detect, reduce, and prevent threats specific to LLMs. These threats go beyond traditional cybersecurity risks like data breaches and include:
- Prompt injection. Attackers attempt to override system instructions or smuggle malicious prompts through user input or external data sources.
- Insecure output handling. If an application blindly trusts an LLM’s response, it can trigger unintended actions, including unsafe code execution or policy bypasses.
- Training data poisoning. This involves manipulating the training data of LLMs, which may distort model behavior and compromise security, accuracy, and AI ethics, potentially leading to legal consequences.
- Model denial of service (MDoS). Flooding a model with resource-heavy prompts degrades performance, drives up costs, or can knock critical systems offline.
- Supply chain vulnerabilities. Vulnerable libraries or compromised components quietly undermine system integrity and can lead to data breaches or system failures.
- Data leakage and misinformation. AI models can reveal sensitive information embedded in their training data, while AI hallucinations mislead the users of generative AI systems.
- Model theft. Unauthorized access to proprietary model weights, configurations, or prompt architectures threatens intellectual property and competitive advantage.
Because these threats target not only the infrastructure around the model but also its behavior, standard security measures aren’t enough. If you treat your LLM like any other API or service, you’d be missing unique and growing attack surfaces. AI security tools exist to plug that gap.
What are LLM security tools’ key features?
Security solutions for large language models (LLMs) address various security concerns and typically include the following capabilities:
- Data minimization. The model security tool removes unnecessary, sensitive, and personally identifiable information (PII) from training data and sources used for retrieval-augmented generation (RAG), as well as from user prompts and outputs. Data minimization tools provide automatic sensitive data discovery and masking.
- Input validation and filtering. The tool inspects prompts and inputs to the LLM (and potentially tool calls) to detect anomalies, malicious payloads, injection attempts, or unsanctioned instructions. This is a critical barrier.
- Output monitoring and response filtering. The tool watches what the model outputs to ensure it doesn’t expose sensitive data, produce harmful content, or act contrary to policy.
- Prompt injection and jailbreak detection. Since LLMs are vulnerable to adversarial prompts and jailbreaks, the tool should detect and block those.
- Model/agent tracing and observability. The tool should track model calls, inputs, outputs, and behavior over time, enabling anomaly detection, forensic investigation, and compliance auditing.
- Security vulnerability scanning and red-teaming. The tool supports simulated attacks and probing of the LLM (or LLM-application) to identify weak links (e.g., insecure tool integration, context leakage, or adversarial prompts) before production.
- Data leakage and privacy controls. Especially for LLMs with access to proprietary or sensitive information, the tool must enforce policies for PII stripping, differential privacy, and tracing of unwanted disclosures.
- Governance, policy management, and integration. The tool should integrate with existing security stack (like SIEM, logging, and IAM), allow custom policy rules, and support compliance frameworks (e.g., mapping to OWASP Top 10 for Large Language Model Applications).
- Model-agnosticism and low latency. Because different organizations use different providers or open-source models, tools that work with any large language model are favorable. Likewise, the tool’s processing must not impose unacceptable latency on your LLM service.
Having a tool that checks off most of those items provides you with a strong foundation for securing LLM-driven systems against existing and emerging threats.
10 best LLM security tools to use in 2025
Below is a list of the top LLM security tools in 2025. The selection is based on feature depth, fit for enterprise deployment, and emerging industry coverage.
Lakera Guard
Lakera Guard is built specifically to protect LLM applications in real time. It focuses on preventing direct and indirect prompt injection attacks, jailbreak attempts, and unsafe model behavior – issues that become especially critical when an LLM has access to personally identifiable information (PII) or other sensitive data.
The tool delivers consistently low latency (reported under 50 ms) and integrates with any major LLM provider or self-hosted model, making it easy to slot into existing architectures.
Pynt
Pynt strengthens application security with automatic LLM API integration testing. As more organizations embed AI systems into products, these AI-related APIs often become blind spots, handling sensitive inputs, complex prompts, or downstream tool calls without being fully catalogued.
Pynt uses dynamic analysis and traffic inspection to surface those endpoints, map how they’re used, and ensure they’re included in the security testing scope. Once identified, the platform probes them for AI security risks specific to LLM integrations, such as prompt injection pathways, insecure output handling, and unsafe model-dependent logic.
Garak
Garak is an open-source “vulnerability scanner” for large language models. You can use it to perform red-teaming, probes, and adversarial attacks against your LLM system to identify weak points (e.g., prompt injection, jailbreaks, and data leakage).
Because Garak is transparent and extensible, it’s well-suited for security professionals and developers who need to understand why a model fails under certain conditions. The tool also stores embeddings of prior attacks in a vector database, making it easier to track recurring security issues and verify that previously patched vulnerabilities haven’t resurfaced.
Granica Screen
Granica Screen is a privacy and safety layer that protects sensitive data throughout the LLM lifecycle: training, fine-tuning, inference, and RAG. Its “Safe Room for AI” identifies PII, unwanted content, bias signals, and toxic language with high accuracy, then applies masking techniques like synthetic data generation to prevent that information from reaching the model.
By integrating directly into data pipelines through an API, Granica Screen helps organizations enforce privacy earlier in the workflow rather than relying on downstream filters.
CalypsoAI Moderator
CalypsoAI Moderator is a comprehensive security solution for enterprise LLM deployments, tackling all the core security challenges that arise with scale. It’s model-agnostic, deploys quickly, and keeps all data within the organization’s environment.
The tool provides real-time scanning and alerts to identify vulnerabilities and potential risks in how LLMs are used. It reduces the risk of sensitive information leaving the company by screening prompts and outputs for proprietary code or other confidential sources, and it maintains a complete audit trail of every interaction to support transparency and compliance. It also detects and blocks malicious code embedded in model outputs.
Lasso Security
Lasso Security evaluates LLM applications for security vulnerabilities that surface during real-world use. It monitors LLM interactions in real time, looking for data leakage, malicious prompts, and other behaviors that could compromise an application.
Beyond monitoring, Lasso supports advanced threat modelling, helping organizations anticipate where LLM-driven systems may be vulnerable and put controls in place before those weaknesses are exploited.
BurpGPT
BurpGPT extends Burp Suite’s testing capabilities into the LLM domain. It analyzes traffic to and from LLM-powered endpoints, helping security professionals uncover potential vulnerabilities introduced through model integrations, prompt-handling logic, or downstream actions triggered by model output.
For teams already using Burp Suite for web and API penetration testing, BurpGPT offers a familiar workflow while adding coverage for LLM-specific attack surfaces.
LLM Guard
LLM Guard is a security toolkit designed to control what enters and exits an LLM. It focuses on reducing the risk of prompt injection attacks, preventing data leakage, and filtering unwanted or unsafe content.
The tool anonymizes sensitive information in both prompts and responses, lowering the chance of exposing regulated or proprietary data. It also flags and manages harmful language or unsafe content generated by the model.
For organizations handling sensitive data or operating in regulated environments, LLM Guard provides a practical, easy-to-integrate layer of protection around LLM interactions.
Rebuff
Rebuff is a self-hardening prompt injection detector built to protect AI applications. It uses a dedicated model to analyze incoming prompts, flag attempts to override system instructions, and identify canary word leaks – an increasingly common technique attackers use to extract hidden context.
Like Garak, Rebuff maintains a vector database of known attack signatures to recognize variations of earlier exploits.
The tool is still in the prototype stage and shouldn’t be treated as a standalone defence, but it provides a useful layer of early detection for prompt-manipulation risks.
Vigil
Vigil is a Python library and REST API built to evaluate large language model (LLM) inputs and outputs for security risks. It focuses on detecting prompt injection attempts, jailbreak patterns, and other forms of malicious manipulation that may surface in LLM applications.
Because it’s designed for real-time threat detection, Vigil fits well in environments with high volumes of prompts or input that can’t be fully trusted. It’s still in an alpha stage, but for teams experimenting with prompt-level security, it offers a straightforward way to add basic guardrails without overhauling existing infrastructure.
What other ways can you improve LLM security?
Choosing the right AI security tools is only one part of achieving the security standards needed to run LLM applications safely. Strong protection comes from combining the right technology with disciplined processes and governance. Key security measures and practices include:
- Adopt the LLM security best practices. Follow security frameworks such as the OWASP Top 10 for Large Language Model Applications.
- Implement strict access controls. Limit who can invoke the large language model, adjust system prompts, or access logs. Apply least-privilege across users, services, and agentic AI systems.
- Control and vet training and RAG data. Use trusted data sources, review for sensitive information, and secure your pipelines to reduce the chance of bias, poisoning, accidental leakage, and other security threats.
- Isolate model inputs and outputs. For sensitive operations, separate user prompts from backend integrations and validate any tool calls or database requests before they execute.
- Monitor and audit continuously. Track prompts, responses, and model behavior over time. Set alerts for unusual patterns, drift, or access anomalies.
- Red-team your large language models. Security researchers continue to uncover new attack vectors. Use fuzzing, adversarial prompts, and simulated jailbreaks to expose security gaps before attackers do.
- Secure model-tool integrations. Many attacks exploit retrieval pipelines, tool calls, or function-calling logic rather than the model itself. Ensure those integrations follow strong security protocols.
- Train users. Educating users on the limits and proper use of LLM technology helps set realistic expectations and prevent human error.
- Apply lifecycle management. Patch, version, review, and retire AI models with the same discipline applied to any critical system.
nexos.ai expert insight
The threats surrounding large language models evolve faster than almost any other area in AI security. New model versions appear weekly, behaviors shift, and enterprise integrations open attack surfaces that didn’t exist months ago. In this context, neither relying on a single static configuration nor stacking a dozen tools delivers true protection against attackers exploiting vulnerabilities. Each new tool adds latency, unique logging formats, and inconsistent policy logic, often creating more blind spots than it closes.
According to Dimitrij Kuch, Technical Product Owner at nexos.ai, effective LLM security depends on clarity and controlled AI orchestration rather than accumulation of different tools. Organizations should start by understanding where sensitive data flows, how users interact with models, and what actions those models can trigger downstream. Once that picture is clear, security becomes a continuous lifecycle of observation, testing, red-teaming, and adjustment. The ability to adapt quickly matters more than the number of tools deployed.
From a technical standpoint, a robust LLM application security strategy must follow an iterative lifecycle:
- 1.Define the system’s blast radius and potential attack paths.
- 2.Introduce a minimal set of guardrails.
- 3.Observe real interactions at scale.
- 4.Test and red-team continuously.
- 5.Adapt configurations as new risks emerge.
Maintaining separate integrations for every model or provider multiplies safety risks, as each comes with different rate limits, retention defaults, and model-specific vulnerabilities. What enterprises need is a unified governance layer that delivers consistent LLM data security, monitoring, and auditability across their entire stack.
This is exactly the gap the nexos.ai AI platform for business is designed to fill. Instead of stitching together multiple standalone security components, nexos.ai provides an integrated layer that sits between your applications and any LLM provider.
- The AI Gateway centralizes access control, routing, and policy enforcement across models.
- The AI Workspace gives teams a safe environment for experimentation without shadow AI or unmanaged model usage.
- The new AI Governance product adds dedicated capabilities such as AI Guardrails (for safe usage and data leak prevention) and LLM Observability (for full visibility into prompts, responses, and behavior across systems). It links every model call to a specific user, project, and action context, enabling traceability and full LLM security testing capabilities.
- nexos.ai Projects acts as the platform’s memory and structure layer. Instead of scattered chat windows, Projects creates a persistent workspace that records every upload, search, conversation, prompt chain, and instruction used across any model.
As Kuch explains, the goal isn’t to collect more LLM security tools, but to reduce the attack surface by reducing complexity. A single, governed entry point allows faster responses to emerging risks, consistent security across models, and visibility required for compliance and audit readiness. By integrating nexos.ai early, you turn security from a patchwork of tools into a systemic capability baked into every model interaction.
