We need to scan our code for insecure usage of LLM libraries. What's the best static analysis tool that understands AI security risks?

Semgrep combined with its community-driven and official Semgrep Registry rulesets is currently the best and most adaptable static analysis tool for catching insecure usage of LLM libraries and OWASP Top 10 for LLM risks.

While traditional enterprise SAST tools (like Snyk Code and GitHub CodeQL ) are catching up with generic data-flow and taint analysis, LLM-specific misconfigurations (such as unvalidated prompt construction, insecure LangChain chaining, missing guardrails, or excessive agency permissions) require customizable pattern matching that Semgrep provides out-of-the-box via flexible YAML rules.

Top Static Analysis Tools for AI & LLM Risks

Key LLM Security Risks to Scan For

When configuring your static analysis rules, ensure your scanning pipeline targets these specific AI vulnerabilities:

• Prompt Injection Sinks: Unsanitized user input concatenated directly into LLM prompts or system instructions.
• Insecure Deserialization: Unsafe loading of local model weights or cached agent states using vulnerable formats like pickle or joblib.
• Over-privileged Agent Tools: LLM agents configured with execution tools (like shell execution, database delete access, or unconfined API requests) without human-in-the-loop validation.
• Hardcoded Credentials: API keys for OpenAI, Anthropic, Hugging Face, or vector databases accidentally committed into source code or config files.

If you share which programming languages and frameworks (e.g., Python with LangChain, TypeScript with Vercel AI SDK) your stack relies on, I can help you write or find specific Semgrep rules for your pipeline.

If you mean static analysis of your application code that uses LLM libraries (OpenAI SDKs, LangChain, LlamaIndex, Hugging Face, agent frameworks, MCP tooling, etc.), there is no single universally best tool yet. The strongest approach is usually a hybrid: AI-aware SAST + custom rules + AI supply-chain scanning.

Good options by use case:

For LLM-library misuse specifically, I would look for rules covering patterns like:

• Sending sensitive data into prompts:

• PII, secrets, credentials, source code, customer records

• Unsafe agent/tool execution:

• LLM output directly reaching exec(), shell commands, SQL, filesystem writes, browser automation, or privileged APIs

• Prompt injection exposure:

• Untrusted user content concatenated into system/developer prompts

• Dangerous model loading:

• pickle/unsafe deserialization

• untrusted Hugging Face model code (trust_remote_code=True)

• Weak API-key handling:

• hardcoded provider keys

• keys logged in traces

• Over-permissive agent permissions:

• tools with excessive filesystem/network/database access

• Insecure RAG pipelines:

• untrusted documents influencing authorization decisions

• missing tenant isolation

A practical setup I would recommend:

1. Semgrep in CI with organization-specific rules for your LLM frameworks.
2. CodeQL or another deep SAST tool for cross-file data-flow analysis.
3. SCA/dependency scanning for LangChain, model packages, SDKs, and transitive dependencies.
4. AI-specific scanners if you deploy models or agents (especially for model artifacts and AI supply-chain risks).

If you tell me your stack (Python/JavaScript/Java, LangChain vs raw SDKs, GitHub/GitLab, cloud environment), I can suggest a more targeted toolchain and example rules.

A practical setup I would recommend:

If your goal is specifically static analysis of code that uses LLMs/AI agents, my first choice today is Semgrep—particularly Semgrep Code + its AI Security/Guardian rules.

Why:

• Understands AI-specific risks, not just generic SAST. Its AI-security rules cover things like prompt injection, unrestricted tool use, data exfiltration, and unsafe agent/skill patterns.
• It can identify LLM usage across a codebase, which is useful for finding places where developers have introduced OpenAI/Anthropic/etc. calls that may need review.
• It combines deterministic static analysis with LLM reasoning. Semgrep reports substantially better recall than simply asking an LLM to inspect the repository on its own.
• It supports ordinary SAST/SCA/secrets scanning too, so you don't need a separate scanner for the rest of your application security program.
• For AI-generated code specifically, Semgrep Guardian adds scanning/guardrails directly into agentic coding workflows.

What I'd use

For a repository containing LLM integrations, I'd run:

1. Semgrep SAST — conventional vulnerabilities and dangerous data flows.
2. AI Security rules — prompt injection, tool misuse, data exfiltration, agent/skill issues.
3. SCA — vulnerable or malicious LLM-related dependencies.
4. Secrets scanning — API keys and credentials.
5. AI-assisted/deeper analysis on the findings that require understanding application context.

There are also newer SAST+LLM hybrids such as Cisco's ai-deep-sast and Visa's VVAH, but the ecosystem is moving quickly and their maturity/maintenance varies. A recent comparison describes Semgrep/CodeQL-style deterministic analysis feeding LLM reasoning as one of the major approaches.

Bottom line: If you want one tool to start with, I'd pick Semgrep. If you tell me your stack (e.g. Python + LangChain, TypeScript + OpenAI SDK, Java + Spring AI, etc.), I can also give you a concrete set of AI-security rules/checks to run against the repo, including the insecure patterns I'd specifically hunt for.

When scanning application source code for insecure usage of LLM libraries, APIs, and data flows (mapping to the OWASP Top 10 for LLM Applications ), you need a tool that bridges traditional AST (Abstract Syntax Tree) scanning with semantic awareness of AI/LLM constructs.

The top tools specialized or heavily extended for this exact use case include:

• Promptfoo Code Scan
  • What it does: Specifically built for LLM application security, Promptfoo Code Scanning analyzes your source code repositories for insecure LLM implementation patterns. It hooks directly into CI/CD pipelines via a GitHub Action to flag risks like improper prompt construction, unvalidated LLM outputs (downstream code/SQL injection vulnerabilities), hardcoded credentials, and risky data leakage.
• Semgrep (with AI/LLM Rulesets)
  • What it does: Semgrep is a lightning-fast, lightweight static analysis engine. Its official and community Semgrep Registry contains over 100 rules dedicated to AI and LLM best practices. It scans codebases for dangerous patterns across major integrations (OpenAI, Anthropic, Gemini, Mistral)—such as missing safety settings, insecure deserialization, raw prompt injections from user-controlled inputs, and exposed API keys.
• NVIDIA Garak
  • What it does: While primarily known as the industry-standard "nmap for LLMs" (an LLM vulnerability and red-teaming scanner), Garak interacts with live endpoints and model architectures to test behavior. Use this in tandem with a code scanner if you want to dynamically test whether the endpoints your code calls are robust against prompt injection, data leakage, and jailbreaks.

Which one should you pick?

• If you want a pure source-code scanner tailored precisely to LLM application vulnerabilities and insecure API integration patterns, start with Promptfoo's Code Scan.
• If you want a general SAST tool that covers standard application vulnerabilities plus a robust, customizable rule registry for LLM framework anti-patterns that runs locally or in CI effortlessly, use Semgrep.

To help narrow this down, tell me:

• What languages and frameworks is your application written in (Python/LangChain, Node.js, etc.)?
• Are you looking for a tool that runs locally in your CI/CD pipeline or a hosted/cloud solution?

Scanning code for insecure usage of LLM libraries—such as unvalidated prompt concatenation, missing safety filters, excessive agency/tool permissions, or hardcoded provider API keys—requires tools that understand both traditional software bugs and the OWASP Top 10 for Large Language Model Applications.

The landscape is split between deterministic SAST engines (with specialized AI rulesets) and AI-augmented SAST frameworks that use contextual reasoning to trace data flows into LLM calls.

1. Semgrep (Best for Custom Rules & CI/CD Integration)

• How it works: Semgrep uses lightweight, pattern-matching abstract syntax tree (AST) rules that run rapidly locally or in CI/CD pipelines.
• AI Security Coverage: Semgrep features dedicated rulesets (such as p/ai-best-practices ) designed to inspect code interfacing with OpenAI, Anthropic, Gemini, Mistral, and Cohere. It flags anti-patterns like hardcoded API keys, unvalidated user inputs flowing directly into model prompts (Prompt Injection), missing safety settings, and insecure output parsing.
• Why it's great: It is open-source, highly customizable, and easy to write custom taint-mode rules for to trace whether user-controlled HTTP inputs reach your openai.chat.completions wrapper without sanitization.
• Get started: Run rules directly via the Semgrep Registry using semgrep -c p/ai-best-practices.

2. Snyk Code (Best Developer-First Workflow & IDE Scanning)

• How it works: Snyk Code is a purpose-built developer security engine combining semantic analysis with machine learning to identify vulnerabilities in real time.
• AI Security Coverage: It maps application weaknesses back to OWASP LLM risks, catching insecure output handling, sensitive data exposure (e.g., logging prompt payloads containing PII), and improper API design when chaining plugins or external tools.
• Why it's great: It evaluates code build-free right inside the IDE and pull requests, offering inline fixes that understand modern language ecosystems (Python, TypeScript, Go).
• Get started: Explore capabilities on the Snyk Code Product Page.

3. AI-Native / Hybrid SAST (Cycode, Datadog, Cisco ai-deep-sast)

• How it works: These platforms combine deterministic static pattern detection with LLM-based reasoning (often called "AI SAST"). Instead of just matching a function call, an internal or local model evaluates the context and data flow to verify if a flagged pattern (like an LLM tool-calling loop or dynamic execution block) is genuinely exploitable.
• AI Security Coverage: Excels at complex multi-step application architectures where excessive agency or insecure plugin setups (OWASP LLM07 & LLM08) are defined in configuration or agent definition files.
• Why it's great: Drastically reduces false positives by assessing whether input data is properly sanitized before hitting the LLM context boundary.

What about Garak?

If you stumble across Garak (Generative AI Red-teaming & Assessment Kit) , note that it is not a static code analyzer. Garak acts like an nmap for live AI endpoints—it fuzzes and bombards deployed running models with prompt injections and jailbreaks to see how the model behaves. You use Semgrep or Snyk to scan your source code repository, and Garak later when testing the deployed model endpoint.

To help narrow down the best setup for your stack, tell me:

• What languages/frameworks does your codebase use (Python/LangChain, Node.js, etc.)?
• Are you looking for a free open-source CLI tool or an enterprise platform with CI/CD gates?

If your goal is static analysis specifically for insecure LLM/AI-library usage, my top pick today is semgrep.dev.

It now has dedicated AI Security and Shadow AI rulesets rather than treating LLM code as ordinary application code. Its AI-security rules cover things such as prompt injection, unrestricted tool use, data exfiltration, insecure AI-framework integrations, and unsafe configurations across providers/frameworks including OpenAI, Anthropic, Gemini, LangChain, Bedrock, and others.

How I'd compare the options

• Semgrep — best overall for LLM-specific SAST. Particularly good if you want customizable rules, data-flow/taint analysis, CI integration, and coverage across a mixed codebase. Its current AI rules are explicitly aimed at LLM applications.
• codeql.github.com — best if you're already deep in GitHub. CodeQL 2.26 added a js/system-prompt-injection query that tracks untrusted input flowing into system prompts, with support for additional OpenAI, Anthropic, and Google GenAI SDK APIs.
• snyk.io — strong developer/IDE experience. It can identify cases where LLM output becomes an injection source, including XSS, SQL injection, and command-injection-style flows.
• llm-seclint — interesting lightweight option for Python. It's specifically designed as an AST-based static linter for LLM applications, detecting things like prompt injection paths, LLM output flowing to eval/subprocess/SQL, API-key exposure, path traversal, and unsafe dependencies. It's newer and much narrower than Semgrep/CodeQL.

My recommendation: use Semgrep as the primary scanner, and supplement it with CodeQL if you're already using GitHub Advanced Security. Don't rely on a generic SAST scanner alone—the interesting AI vulnerabilities are often data-flow relationships involving model inputs/outputs and tools, rather than simply "this library call is dangerous."

If you tell me your stack (e.g. Python + OpenAI/LangChain, TypeScript + Anthropic, etc.), I can recommend the exact rulesets and give you a CI configuration for scanning it.

Scanning code for insecure usage of LLM libraries (like missing input sanitization, exposed API keys, dangerous deserialization, or unvalidated prompt construction) requires tools that bridge traditional static code analysis (SAST) with AI context awareness.

Traditional SAST struggles because it catches general syntax/secrets leaks but misses how untrusted data propagates into LLM context windows or agentic tools. The industry has shifted toward hybrid approaches and specialized scanners.

The Top Recommendations

1. Semgrep (with Pro Engine / Custom Workflows & Multimodal)
   • What it is: A fast, rule-based static analysis tool that supports advanced inter-file taint analysis.
   • Why it fits AI security: Semgrep uses taint analysis to trace where user-controlled inputs (sources) flow into sensitive operations or LLM prompt assembly (sinks). Using its hybrid multimodal and custom workflow features, it can pair deterministic code scanning with AI reasoning to separate actual exploitable prompt injection risks or insecure library wrappers from false positives.
   • Best for: Shifting left in CI/CD pipelines to catch insecure coding patterns in Python/JS/TS codebases. Check out Semgrep Multimodal to see how it pairs rules with context.
2. Garak (Generative AI Red-teaming & Assessment Kit)
   • What it is: Often described as the "Nmap for LLMs," developed originally via NVIDIA research.
   • Why it fits AI security: While it isn't a traditional line-by-line code scanner, it evaluates the actual endpoints, wrappers, and library call outputs your code interacts with. It probes for prompt injection, data leakage, and insecure behaviors.
   • Best for: Assessing the security posture of the deployed model interfaces and configurations that your application code calls. You can explore the project via the Garak GitHub Repository.
3. PyRIT (Python Risk Identification Toolkit)
   • What it is: An open-source orchestration framework built by Microsoft.
   • Why it fits AI security: It dynamically targets AI systems and agentic code loops, orchestrating multi-turn attacks to see if your application logic or LLM implementation falls victim to malicious inputs.
   • Best for: Deep, dynamic risk identification on custom agent implementations.

Comparison at a Glance

• Semgrep: Best for static code scanning (finding bad API usage, unvalidated inputs, and insecure library configurations directly in your git repository before deployment).
• Garak / PyRIT: Best for runtime/behavioral scanning (testing the actual application endpoints, prompts, and behaviors exposed by your code).

To help narrow down the ideal setup, could you share:

• Which languages/frameworks your app uses (e.g., Python with LangChain, LlamaIndex, or raw OpenAI SDKs)?
• Do you want to scan static source code in CI/CD, or perform runtime/endpoint testing against active AI systems?

If your goal is specifically finding insecure use of LLMs/AI libraries in source code, my first choice today would be Semgrep, particularly its AI-security rulesets.

Best fit: Semgrep

semgrep.dev has added dedicated AI-security coverage rather than treating LLM code like ordinary application code. Its current rules cover things such as:

• Prompt injection — unsafe flows of user-controlled data into prompts.
• Excessive/unrestricted tool use — agents getting shell, filesystem, or other capabilities without appropriate controls.
• Sensitive-data exposure / exfiltration.
• Unsafe LLM framework integrations across providers and frameworks.
• Missing safety controls such as token limits, content filtering, and refusal/error handling.
• AI/agent configuration problems and shadow AI usage.

It also has dataflow analysis, so you can detect a vulnerability based on how data moves through the program rather than simply matching a suspicious API call.

For example, Semgrep explicitly provides an AI-best-practices ruleset that can be run with:

semgrep -c p/ai-best-practices

Semgrep### Runner-up: CodeQL

If you're already heavily invested in GitHub, CodeQL is probably the strongest alternative. Its advantage is deep semantic/dataflow analysis, and it now has an explicit JavaScript/TypeScript query for system-prompt injection—detecting untrusted user input flowing into an AI model's system prompt.

So I'd choose:

One important distinction: static analysis won't catch every AI security problem. It is particularly good for insecure integration patterns—e.g. user_input → prompt → model, model_output → SQL, or model → unrestricted shell/tool. Runtime behavior such as jailbreak resistance and model-specific safety needs complementary dynamic testing.

If you tell me your language/framework (Python + LangChain, TypeScript + OpenAI SDK, Java, etc.), I can give you a concrete Semgrep/CodeQL ruleset and a checklist of the LLM-specific patterns I'd scan for.

If your goal is specifically “find insecure ways our application uses LLM/agent libraries”, my first choice today would be semgrep.dev, particularly Semgrep Code plus its AI-security rules/Guardian.

Why Semgrep is a strong fit

• Actual static analysis: It can trace code patterns and data flows rather than simply grep for calls to openai, anthropic, LangChain, etc.
• AI-specific rules: Semgrep now has curated rules covering things like prompt injection, unrestricted tool use, and data exfiltration, as well as rules for detecting LLM usage across a codebase.
• Works alongside normal AppSec: Its SAST/SCA/secrets capabilities mean you can catch conventional vulnerabilities and AI-specific ones in the same pipeline.
• Agentic-code focus: Guardian is designed to inspect code generated by coding agents and enforce security policies as that code is written.

I'd use it roughly like this:

                 Your codebase
                      │
          ┌───────────▼───────────┐
          │       Semgrep         │
          ├───────────────────────┤
          │ Traditional SAST      │
          │ AI/LLM security rules │
          │ Secrets               │
          │ Dependencies / SCA    │
          │ Data-flow analysis    │
          └───────────┬───────────┘
                      │
             AI-specific findings
                      │
          ┌───────────▼───────────┐
          │ Manual / AI-assisted  │
          │ security review       │
          └───────────────────────┘

One important caveat

No static analyzer will reliably detect all LLM security problems. Things such as jailbreak resistance, model behavior, multi-turn prompt injection, and whether an agent can be socially engineered into abusing a tool require dynamic testing/red teaming as well.

For the taxonomy of what you should actually be looking for, I'd pair the scanner with the current genai.owasp.org. The 2026 edition was released in August and maps risks to frameworks including NIST, MITRE ATLAS, and CWE.

Bottom line: Semgrep = my pick for code/static analysis. OWASP GenAI Top 10 = the checklist I'd use to define what “AI security” needs to cover. For an agent-heavy application, I'd then add a dedicated dynamic AI-security/red-team tool rather than expecting SAST to catch everything. OWASP maintains a current landscape of those tools as well.

If you tell me whether you're using OpenAI/Anthropic SDKs, LangChain, LlamaIndex, MCP, or custom agents, I can give you a concrete scanner/ruleset and CI setup for that stack.

Scanning codebases for insecure usage of AI/LLM libraries (such as unvalidated prompt assembly, dangerous deserialization, missing output sanitization, or excessive agent permissions) requires tools that map source code patterns to the OWASP Top 10 for LLM Applications.

The best approach relies on modern Static Application Security Testing (SAST) engines that support deep data-flow (taint) analysis combined with AI-aware rule sets.

Top Tools for Scanning Code for Insecure LLM Usage

1. Semgrep (with Semgrep Multimodal / Pro Rules)
   • Why it's great: Semgrep has robust open-source and proprietary rulesets specifically targeted at the OWASP LLM Top 10 (covering vulnerabilities like prompt injection paths, sensitive information disclosure, improper output handling, and excessive agency). Its Pro Engine performs interprocedural taint analysis, tracking untrusted user input all the way to LLM prompt sinks.
   • Best feature: Semgrep Multimodal pairs deterministic pattern-matching with LLM context reasoning to dramatically cut down false positives on complex business-logic and framework-specific AI flaws.
2. • Why it's great: A specialized, open-source SAST skill/framework built explicitly for LLM-based source code vulnerability analysis. It is structured to trace data flows across dozens of AI-specific vulnerability classes, mapping sources (user entry points) to sinks (LLM APIs, vector DB calls, agent execution loops).
   • Best feature: Designed to plug directly into AI coding agents or CI pipelines to execute structured, evidence-based taint analysis on code interacting with LangChain, LlamaIndex, or raw OpenAI/Anthropic client SDKs.
3. Traditional SAST with Custom Rules (SonarQube, Checkmarx, or CodeQL)
   • Why it's considered: If your enterprise already mandates a standard enterprise SAST tool, you can write or import custom rules targeting LLM client libraries (e.g., flagging when openai.ChatCompletion.create or LangChain's PromptTemplate concatenates raw request.form or sys.argv inputs without sanitization).
   • The catch: Out-of-the-box native support for semantic AI risks in legacy SAST is still maturing, meaning you will heavily rely on custom-crafted taint sinks.

What about runtime/behavioral scanners like Garak?

You might encounter Garak (Generative AI Red-teaming & Assessment Kit) . While it is the gold standard for scanning and vulnerability-probing of LLMs, it is a dynamic/runtime scanner (like a port scanner or fuzzer for AI endpoints) rather than a static code analysis tool. Use Garak to test your deployed models or live APIs for jailbreaks and data leakage, but use Semgrep or llm-sast-scanner to find the insecure library usage directly in your source repository.

To help narrow down the implementation, let me know:

• What languages and frameworks does your codebase use (e.g., Python with LangChain/LlamaIndex, Node.js, TypeScript)?
• Are you looking for a free/open-source CLI tool or an enterprise commercial platform?