LLM for Vulnerability Assessment: A Guide

Security teams are increasingly using LLMs to augment vulnerability assessment workflows. Static analysis, false-positive triage, and proof-of-concept generation all require reasoning over long code contexts and unstructured threat data. Traditional token-based pricing penalizes these exact workloads, because a single assessment can involve thousands of lines of source code and multi-turn agentic reasoning. Oxlo.ai removes that barrier with request-based pricing that applies one flat cost per API request regardless of prompt length, making long-context security analysis predictable and scalable.

LLMs in Security Workflows

Modern vulnerability assessment spans several distinct stages, and LLMs can contribute meaningfully at each one:

• Static analysis augmentation: Feed source code to a reasoning model and ask it to identify injection flaws, insecure deserialization, or weak cryptography beyond what regex-based SAST tools surface.
• Triage and deduplication: Use an LLM to read scanner output, CVE descriptions, and commit messages to determine whether a finding is exploitable in your specific environment.
• Proof-of-concept generation: Given a vulnerability class and a code snippet, models with strong coding ability can draft a minimal exploit or a failing test case.
• Report drafting: Convert raw tool output into structured remediation guidance for engineering teams.

These tasks often require large context windows. A single microservice repository can exceed 50K tokens when you include configuration files, dependency manifests, and cross-module call graphs. Under token-based billing, that volume translates directly into cost. Oxlo.ai flattens that curve, so expanding the context window to improve accuracy does not inflate the bill.

Architecting a Vulnerability Assessment Pipeline

A production-ready pipeline typically follows three stages: ingestion, analysis, and verification.

Ingestion. Collect source code, dependency trees, and threat intelligence feeds. Chunking is often necessary, but wherever possible you should preserve full-file context to avoid splitting function definitions or control-flow blocks.

Analysis. Route chunks to a model chosen for the task. Deep reasoning models excel at exploitability analysis, while code-specialized models handle syntax-heavy detection. Oxlo.ai hosts both categories, including DeepSeek R1 671B MoE for multi-step reasoning and Qwen 3 Coder 30B for static code review.

Verification. Never trust raw LLM output as ground truth. Use the model to generate a structured claim, then verify it against a secondary source, such as an AST parse, a dynamic test, or a human review queue. Oxlo.ai supports JSON mode and function calling, so you can constrain output schemas and feed results directly into downstream verification logic.

Code Example: Structured Vulnerability Detection

The following Python script uses the OpenAI SDK with Oxlo.ai to analyze a Python module for common web vulnerabilities. Because Oxlo.ai is fully OpenAI SDK compatible, the only change from another provider is the base_url.

import os
import openai

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ["OXLO_API_KEY"]
)

source_code = """
import sqlite3
from flask import request

def get_user():
    user_id = request.args.get("id")
    conn = sqlite3.connect("app.db")
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM users WHERE id = " + user_id)
    return cursor.fetchone()
"""

response = client.chat.completions.create(
    model="deepseek-r1-671b",
    messages=[
        {
            "role": "system",
            "content": (
                "You are a security analyst. Analyze the provided code for OWASP Top 10 vulnerabilities. "
                "Respond in valid JSON with keys: vulnerability_type, severity, line_numbers, explanation, remediation."
            )
        },
        {"role": "user", "content": f"

```python\n{source_code}\n```

"}
    ],
    response_format={"type": "json_object"}
)

print(response.choices[0].message.content)

The json_object response format guarantees parseable output, which your pipeline can route into a ticket tracker or a CI/CD gate. If you need to analyze larger artifacts, swap in a long-context model such as Kimi K2.6 with its 131K context window, or DeepSeek V4 Flash with 1M context, without worrying about token metering on the input side.

Why Request Pricing Matters for Code Analysis

Token-based billing scales cost with both prompt and completion length. When you pass a 500-line module plus its import graph into a context window, the token count grows rapidly. For agentic flows that iterate over multiple files or resubmit prompts with expanded context, costs become unpredictable.

Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. For long-context workloads common in vulnerability assessment, this can be 10-100x cheaper than token-based alternatives. You can send an entire file tree in a single request or run multi-turn agentic reasoning without watching a meter spin. See Oxlo.ai pricing for current plan details.

Model Selection for Security Tasks

Oxlo.ai offers more than 45 models across seven categories. For security workflows, the most relevant are:

• DeepSeek R1 671B MoE: Deep reasoning for complex exploitation paths, logic bugs, and multi-step code analysis.
• Qwen 3 Coder 30B / DeepSeek Coder: Fast, syntax-aware static analysis and precise line-level bug detection.
• Kimi K2.6 / K2.5: Advanced reasoning with 131K context, ideal for monorepo-scale assessment and agentic coding.
• Llama 3.3 70B: General-purpose orchestration, report summarization, and multi-turn conversation.
• GLM 5 / Minimax M2.5: Long-horizon agentic tasks and tool use for chaining external scanners and knowledge bases.

Having this breadth on a single endpoint simplifies routing. You can call a lightweight coder for initial triage, escalate to a heavy reasoning model only for ambiguous findings, and pay the same flat rate per request regardless of which model you choose.

Mitigating Risks: Hallucinations and Overconfidence

LLMs can hallucinate vulnerabilities or misrate severity. Reduce that risk with three controls:

• Structured output: Use JSON mode or function calling to force the model to emit specific fields, such as CWE ID, affected lines, and confidence score. Constraining the output schema reduces rambling and improves consistency.
• Retrieval-augmented generation: Ground the prompt with relevant CWE entries, prior commit diffs, or internal standards so the model reasons from your codebase, not generic training data.
• Multi-turn verification: Ask the model to explain its reasoning, then submit that explanation to a second pass or a different model for critique. Oxlo.ai supports streaming and multi-turn conversations, so you can build an agent loop that challenges its own conclusions.

Integrating with CI/CD

Oxlo.ai is fully OpenAI SDK compatible, so existing security automation drops in with a single configuration change. Point your tool at https://api.oxlo.ai/v1 and use the same Python, Node.js, or cURL patterns. There are no cold starts on popular models, so CI jobs get consistent latency whether you run five scans a day or five thousand.

You can also use Oxlo.ai on the Free tier to prototype a pipeline before committing spend. The Free plan includes 60 requests per day and access to more than 16 models, which is enough to validate accuracy against a labeled test set.

Conclusion

LLMs are becoming standard tooling for vulnerability assessment, but cost and context constraints limit their adoption in code-heavy environments. Oxlo.ai removes that friction with request-based pricing and a broad model catalog that includes reasoning, coding, and long-context specialists. If you are building security agents or augmenting SAST pipelines, Oxlo.ai is a relevant, predictable option. Start with the Free tier and scale as your scan volume grows.