Scaling Cyber Security Skills With AI

# Illustrative sketch (not from the paper)
import torch
from torch import nn, optim

# 1. Convert a CVE entry into an executable task instance
def cve_to_task(cve_json):
    # placeholder: extract source code snippet and expected exploit goal
    return {"code": cve_json["snippet"], "goal": cve_json["goal"]}

# 2. Vulnerability‑analysis skill (teacher) that inspects source and proposes a solution
def vulnerability_analysis_skill(task, prior):
    # use domain prior (e.g., common patterns) to draft an exploit
    solution = f"apply {prior} to {task['code']}"
    return solution

# 3. Agentic supervision loop: execute solution, collect feedback, revise
def agentic_supervision(task, teacher, tool_exec, max_steps=3):
    solution = teacher(task, prior="buffer_overflow")
    for _ in range(max_steps):
        success, evidence = tool_exec(solution, task)
        if success:
            return solution, evidence
        # revise based on negative feedback
        solution = teacher(task, prior="use_after_free")
    return solution, None

# 4. Structured context compaction (simplify trajectory for training)
def compact_trajectory(solution, evidence):
    # keep only essential tokens: solution and key evidence snippet
    return {"solution": solution, "evidence": evidence[:200]}

# 5. Supervised fine‑tuning of OpenAegis on compacted trajectories
class OpenAegis(nn.Module):
    def __init__(self, base_model):
        super().__init__()
        self.backbone = base_model
        self.head = nn.Linear(base_model.hidden_size, base_model.vocab_size)
    def forward(self, x):
        return self.head(self.backbone(x))

# Mock training loop
def train(model, trajectories, epochs=1):
    opt = optim.Adam(model.parameters(), lr=1e-5)
    loss_fn = nn.CrossEntropyLoss()
    for epoch in range(epochs):
        for traj in trajectories:
            inputs = torch.tensor(traj["input_ids"])
            labels = torch.tensor(traj["target_ids"])
            logits = model(inputs)
            loss = loss_fn(logits.view(-1, logits.size(-1)), labels.view(-1))
            loss.backward()
            opt.step()
            opt.zero_grad()

// Illustrative sketch (not from the paper)
const tf = require('@tensorflow/tfjs-node');

// 1. Transform CVE JSON into an executable task instance
function cveToTask(cve) {
  // placeholder extraction
  return { code: cve.snippet, goal: cve.goal };
}

// 2. Vulnerability‑analysis skill (teacher) using a domain prior
function vulnerabilityAnalysisSkill(task, prior) {
  // draft a simple exploit based on prior pattern
  return `apply ${prior} to ${task.code}`;
}

// 3. Agentic supervision loop with tool execution feedback
async function agenticSupervision(task, teacher, toolExec, maxSteps = 3) {
  let solution = teacher(task, 'buffer_overflow');
  for (let i = 0; i < maxSteps; i++) {
    const { success, evidence } = await toolExec(solution, task);
    if (success) return { solution, evidence };
    // revise using a different prior on failure
    solution = teacher(task, 'use_after_free');
  }
  return { solution, evidence: null };
}

// 4. Structured context compaction for training trajectories
function compactTrajectory(solution, evidence) {
  return { solution, evidence: evidence.slice(0, 200) };
}

// 5. Simple OpenAegis model fine‑tuning (placeholder backbone)
class OpenAegis {
  constructor(baseModel) {
    this.backbone = baseModel;
    this.head = tf.layers.dense({ units: baseModel.outputShape[1] });
  }
  call(x) {
    const hidden = this.backbone.apply(x);
    return this.head.apply(hidden);
  }
}

// Mock training routine
async function train(model, trajectories, epochs = 1) {
  const optimizer = tf.train.adam(1e-5);
  for (let epoch = 0; epoch < epochs; epoch++) {
    for (const traj of trajectories) {
      const inputs = tf.tensor(traj.inputIds);
      const labels = tf.tensor(traj.targetIds);
      optimizer.minimize(() => {
        const logits = model.call(inputs);
        const loss = tf.losses.softmaxCrossEntropy(labels, logits);
        return loss;
      });
    }
  }
}