> Configuring your AI vulnerability harness, Part 2: The steering file

This post shows you how to configure an AI model to perform structured, evidence-based vulnerability triage with the consistency of a seasoned security analyst. You’ll learn the design decisions behind five configuration sections that enforce structural verification, evidence-based scoring, and infrastructure-aware prioritization across every analysis session. Our companion post—Building your AI vulnerability harness—covered the architecture; this post covers the configuration. The distinction matters because the same frontier model that produces a rigorous, evidence-grounded security assessment with one set of instructions will produce hallucinated attack chains and inflated severity ratings with another. The model’s capability is fixed. What you configure is its judgment. We learned this the hard way while building the harness. Our early iterations produced findings that sounded authoritative but crumbled under scrutiny. The model would report a SQL injection in a function that didn’t exist, claim HIGH confidence based on nothing structural, or ignore an AWS WAF in blocking mode directly in the request path. The output wouldn’t have earned engineering trust in that state. The fix wasn’t a better model. It was better steering. What a steering file is A steering file is a set of instructions that an AI coding assistant loads at the start of every session. Different tools use different conventions—Kiro reads markdown files from a .kiro/steering/ directory, Claude Code uses CLAUDE.md, Cursor uses .cursorrules, and GitHub Copilot uses .github/copilot-instructions.md—but the function is the same: persistent instructions that shape how the model approaches work in your repository. Applied to security analysis, a steering file encodes your team’s triage methodology as machine-executable instructions. Instead of relying on individual engineers to apply that methodology consistently, you encode it once and the model applies it uniformly across analyses. The steering file we’re releasing encodes the architectural principles from the harness post as operational instructions. Where the harness post says apply infrastructure-aware assessment, the steering file specifies exactly how: which controls map to which multipliers, how multipliers stack, and what floor prevents over-confidence in any single control. Why configuration outperforms prompting A single-turn prompt (find vulnerabilities in this code) produces whatever the model’s default behavior generates. That default is optimized for helpfulness, not rigor. The model will find things to report because you asked it to find things. A steering file changes the default. It establishes: What counts as evidence: Without explicit instructions, a model treats its own reasoning as sufficient evidence. The steering file requires structural verification—confirmed dataflow, confirmed function existence, and confirmed call path—before a finding is reported. What confidence means: On its own, a model assigns confidence based on how plausible its narrative sounds to itself. The steering file replaces self-reported confidence with a formula computed from binary structural signals. Either taint analysis confirms the flow or it doesn’t. Either a call graph connects entry point to sink or it doesn’t. How infrastructure affects priority: Left to its defaults, a model assesses vulnerabilities in isolation from their deployment context. The steering file requires parsing infrastructure as code (IaC) and applying control-specific multipliers before assigning priority. The goal is reproducibility: the same codebase should produce the same assessed findings regardless of who runs the analysis or when. Consistency is the point. The five sections that matter The full steering file is available in the companion repository. In this section, we explain the design decisions behind its five critical sections. Structural verification over LLM trust Never trust an LLM's self-reported confidence about a vulnerability. Verify claims against the code structure: 1. Do referenced files exist? 2. Do referenced functions exist? 3. Does dataflow confirm the claim? 4. Is there a call path? If a finding fails all structural checks, reject it regardless of how convincing the narrative sounds. This is the single most important instruction in the file. Without it, the model generates plausible-sounding attack chains that reference functions that were renamed three commits ago, files that exist in a different package, or dataflows that pass through sanitization that the model failed to notice. We found that approximately 30% of unsteered model findings referenced code structures that didn’t exist in the target repository. Not wrong interpretations—entirely fabricated paths. The instruction to verify before reporting produced zero fabricated paths in our testing. The key insight: hallucination in security analysis isn’t a minor annoyance. One hallucinated finding that reaches a developer destroys the can quickly erode the credibility of your entire pipeline. Engineers who encounter a fabricated vulnerability are less likely to keep reading the reports. Evidence-based confidence scoring The following formula replaces the model’s intuitive confidence with a score computed from observable signals. Each factor is binary: confirmed or not confirmed. confidence = 0.30 * taint_confirms_flow + 0.25 * no_sanitization_found + 0.20 * entry_point_is_public + 0.15 * call_graph_confirms_path + 0.10 * sink_type_matches_category We weighted taint confirmation highest (0.30) because a confirmed dataflow path—evidence that user-controlled data reaches a security-sensitive operation—is the single strongest predictor that a finding is real. In our validation against known-vulnerable applications, findings with confirmed taint and no sanitization were exploitable 89% of the time. Findings where the model reported HIGH confidence without structural confirmation were exploitable 34% of the time. The formula is a starting point, not a fixed standard. Your weights should reflect your own validation data. Run the formula against a labeled dataset of known true and false positives, then adjust until precision matches your team’s tolerance for noise. Infrastructure-aware assessment This section addresses a common category of over-prioritization: reporting a vulnerability at full severity when compensating controls are already in place. score_after_controls = max(0.15, score * block_1 * block_2 * ... * block_n) Controls are mapped to specific vulnerability classes. An AWS WAF with SQL injection rules in blocking mode is a STRONG control (0.25x multiplier) against SQL injection. It is irrelevant to deserialization attacks. The steering file specifies this mapping explicitly rather than leaving the model to guess. The multiplicative stacking of attack-blocking controls with a floor of 0.15 encodes defense-in-depth thinking. Multiple controls reduce risk more than any single control, but no combination of controls reduces risk to zero. The floor prevents the model from concluding that a vulnerability is completely mitigated and can be ignored. A subtlety we learned through validation: access controls (authentication and authorization) and attack-blocking controls (AWS WAF rules and input validation) serve different functions. Authentication limits who can attempt an exploit. It doesn’t limit what happens when an authenticated user attempts one. A command injection behind AWS Identity and Access Management (IAM) authentication is still a command injection; the attacker pool is smaller, but the impact when exploited is unchanged. The steering file accounts for this by mapping control types to specific score components rather than applying a blanket multiplier. Threat intelligence integration The steering file incorporates signals from the CISA Known Exploited Vulnerabilities (KEV) catalog and the Exploit Prediction Scoring System (EPSS) to boost priority when threat intelligence indicates active risk. The following table lists the primary signals the steering file recognizes and the boost that each signal adds to a finding’s score. Each signal answers a different question about real-world exploitation risk: CISA KEV – Indicates that attackers have exploited the vulnerability in the wild and notes whether it is tied to ransomware campaigns. EPSS – Estimates the probability that attackers will exploit a vulnerability in the next 30 days. Public proof of concept (PoC) – Indicates that working exploit code is already published, so an attacker has little work left to do. Signal Boost Actively exploited (CISA KEV) +0.30 Ransomware-associated (CISA KEV) +0.15 EPSS >= 0.5 +0.20 Public PoC exists +0.10 There’s a cap at +0.50 total boost to prevent threat intelligence from dominating the score. A vulnerability isn’t more technically exploitable because attackers are targeting it, but it is more urgent because the window between theoretically exploitable and actively exploited can be very short. We separate urgency from severity deliberately. A medium-severity dependency vulnerability under active exploitation by ransomware groups demands a faster response than a critical-severity code-level finding that requires complex preconditions and has no known tooling. Priority classification Scoring produces a number and priority classification turns that number into a decision, which is what a developer on the receiving end of a finding needs. The following table shows the four action tiers that the steering file defines. The score and evidence thresholds determine which tier a finding falls into, and each tier prescribes a specific response. The steering file compares these thresholds against the final score: the base score after it applies attack-blocking multipliers and any threat-intelligence boost. That final score is not the confidence value from the formula earlier in this post, and it is not the severity that the scanner reported. Priority Criteria Action P0 Score >= 0.8, no effective attack-blocking mitigation Fix immediately P1 Score >= 0.6 OR active threat intel Fix this sprint P2 Score >= 0.4, partial evidence Investigate when capacity P3 Score < 0.4 or effectively mitigated Accept risk or backlog The most controversial instruction in the file: Do not file P3 findings as security issues. We include it because filing low-confidence or fully mitigated findings as tickets trains teams to ignore security tooling. Each false positive or irrelevant finding filed as a bug reduces engagement with the system’s output. A pipeline that produces ten confirmed findings gets more engineering attention than one that produces two hundred maybes. Validation results We tested the steering file against a purpose-built vulnerable application containing ten known vulnerabilities: eight exploitable and two mitigated by infrastructure controls. The application includes an AWS WAF with SQL injection rules in blocking mode, Amazon Virtual Private Cloud (Amazon VPC)-isolated AWS Lambda functions, and IAM authentication on each endpoint. Without the steering file, the model found all ten vulnerabilities and correctly identified both mitigated findings as lower priority. However, it assigned severity based on intuition (this is a command injection, so it’s CRITICAL) rather than evidence, and it produced no structural verification of its claims. With the steering file, the model found nine of ten vulnerabilities (not flagging a hardcoded credential that had been intentionally embedded in unit-testing code and was defined but never called), correctly downgraded the two mitigated findings to P3 with documented rationale, showed its confidence calculation for each finding, and verified each claimed dataflow against the actual code. The scoring calibration required one iteration. Our initial version applied authentication as a blanket multiplier that suppressed all findings behind IAM to P2 regardless of impact. We corrected this by separating access-limiting controls (which reduce the reachability score component) from attack-blocking controls (which reduce the full confidence score). The current version correctly classifies a command injection behind IAM authentication as P0: the auth limits who can reach it, but the impact when exploited is remote code execution regardless. How to use the steering file The three options that follow cover an always-on Kiro steering file, the same content as an on-demand Kiro skill, and how to carry the same content to Claude Code or another assistant. Option 1: As a Kiro steering file Drop the steering file into your repository at .kiro/steering/vuln-triage.md. Any session using Kiro’s built-in default agent loads the instructions automatically. When you or your engineers ask the model to analyze code for security issues, it applies the methodology without additional prompting. If your team uses a custom agent rather than the default, steering files aren’t loaded automatically; you have to add the file to the agent’s resources explicitly: { "resources": ["file://.kiro/steering/**/*.md"] } your-repo/ ├── .kiro/ │ └── steering/ │ └── vuln-triage.md ← steering file ├── src/ ├── cdk/ └── ... You can also place it in ~/.kiro/steering/ to apply the methodology across every workspace on your machine, rather than scoping it to a single repository. Option 2: As a Kiro skill If you want the methodology available on demand rather than consuming context window space in every turn, place it as a skill. Kiro loads only the skill’s name and description at startup and pulls in the full instructions when the skill activates. This keeps the instructions out of unrelated conversations. A skill is a folder containing a SKILL.md file. The folder name must match the name in the YAML front matter: name: vuln-triage description: Structured vulnerability triage methodology with evidence-based scoring and infrastructure-aware prioritization. Use when analyzing code for security vulnerabilities. your-repo/ ├── .kiro/ │ └── skills/ │ └── vuln-triage/ │ └── SKILL.md ← skill file ├── src/ └── ... Kiro’s default agent discovers skills in .kiro/skills/ automatically; no configuration required. The skill then activates either automatically, when Kiro matches a request against the description, or explicitly as a slash command. A skill named vuln-triage becomes /vuln-triage, so an engineer can run an assessment on demand: > /vuln-triage focus on the authentication changes Place skills in ~/.kiro/skills/ to make them available across every project on your machine. If your team uses a custom agent rather than the default, skills aren’t loaded automatically, you must add them to the agent’s resources: { "resources": ["skill://.kiro/skills/*/SKILL.md"] } Option 3: Adapted for other tools The methodology isn’t specific to Kiro. The repository also ships the same content as a CLAUDE.md file for Claude Code, and the principles apply to any assistant that loads persistent instructions from your repository—consult your tool’s documentation for the filename and location it expects. What this doesn’t replace A steering file is methodology, not machinery. It tells the model how to think about findings but doesn’t provide: Programmatic taint tracking: The model approximates dataflow by reading code. A purpose-built taint tracker confirms dataflow against the abstract syntax tree (AST) deterministically. For Python codebases, static analysis tools built on parsers like Tree-Sitter provide this. For other languages, the model’s approximation is reasonable but not authoritative. Live infrastructure verification: The steering file instructs the model to parse IaC files. It can’t make API calls to verify that an AWS WAF rule is attached to the Amazon API Gateway in your deployed environment, or that a security group hasn’t been modified since deployment. Threat intelligence feeds: The file instructs the model to consider CISA Known Exploited Vulnerabilities (KEV) and Exploit Prediction Scoring System (EPSS) data. The model’s training data includes historical KEV entries, but it can’t query live feeds. For current exploitation status, you need an API integration. Execution-based verification: The steering file produces assessed findings. Confirming exploitability by executing a proof of concept against a live environment requires additional tooling: a sandbox, request signing, and differential testing infrastructure. Each of these capabilities adds confidence to the pipeline. The steering file without them still produces measurably better results than an unsteered model: structured findings, cited evidence, and infrastructure-aware prioritization. But it represents the first layer of a mature harness, not the complete system. What comes next The steering file gives your team a structured methodology for AI-powered vulnerability triage today. It works with the tools you already have. It works without infrastructure changes, new services, or a procurement process. The steering file is available in our GitHub repository. We encourage you to use it, adapt it to your environment, and let us know what you find. The authors work on security automation at AWS. The patterns described here emerged from building and validating AI-powered vulnerability detection systems across multiple teams and deployment environments. If you have feedback about this post, leave a comment in the Comments section below. Justin Kontny Justin is a Senior Software Engineer, Security, at AWS who blends a passion for software development with deep expertise in cloud security. He focuses on building agentic security solutions using AI-Driven Development Lifecycle (AIDLC) practices, transforming security from a barrier into a business enabler. Outside of work, Justin enjoys spending time with his children and staying active outdoors. Ievgeniia Ieromenko Ievgeniia is a Software Development Engineer at AWS, where she builds tooling and agentic AI systems designed to strengthen security posture without slowing delivery. Outside of work, she enjoys traveling, volunteering, and outdoor adventures with her very good dog, Pickle. Nidhi Ramakant Nidhi is a Software Development Manager at AWS with two decades of experience in security and enterprise architecture. She’s obsessed with helping customers secure their environments and building creative solutions that make security decisions easier. She’s equally invested in developing the talent around her. Outside work, she’s either binging shows or vibe coding apps for secure local LLMs so her kids can explore and learn safely.