RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale | Signal Canvas | ScienceToStartup

Page Freshness

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Canonical route: /signal-canvas/ruleforge-automated-generation-and-validation-for-web-vulnerability-detection-at-scale

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID ruleforge-automated-generation-and-validation-for-web-vulnerability-detection-at-scale | Route /signal-canvas/ruleforge-automated-generation-and-validation-for-web-vulnerability-detection-at-scale

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/ruleforge-automated-generation-and-validation-for-web-vulnerability-detection-at-scale

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

PDF: https://arxiv.org/pdf/2604.01977v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.576Z

Signal Canvas receipt window

Watch and verify: RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

/buildability/ruleforge-automated-generation-and-validation-for-web-vulnerability-detection-at-scale

Watchwatch

Subject: RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
We present RuleForge, an AWS internal system that automatically generates detection rules--JSON-based patterns that identify malicious HTTP requests exploiting specific vulnerabilities--from structured Nuclei templates describing CVE details.
Implicationpartial
Directly stated in abstract with clear description of system functionality
Verificationpartial
partial
Evidencepartial
This validation approach evaluates candidate rules across two dimensions--sensitivity (avoiding false negatives) and specificity (avoiding false positives)--achieving AUROC of 0.75
Implicationpartial
Directly stated in abstract with specific numeric metric
Verificationpartial
partial
Evidencepartial
reducing false positives by 67% compared to synthetic-test-only validation in production.
Implicationpartial
Directly stated in abstract with clear numeric improvement
Verificationpartial
partial
Evidencepartial
In 2025, the National Vulnerability Database published over 48,000 new vulnerabilities, motivating the need for automation.
Implicationpartial
Directly stated in abstract with specific statistic
Verificationpartial
partial
Evidencepartial
Our 5x5 generation strategy (five parallel candidates with up to five refinement attempts each) combined with continuous feedback loops enables systematic quality improvement.
Implicationpartial
Directly stated in abstract with specific technical details
Verificationpartial
partial
Evidencepartial
We also present extensions enabling rule generation from unstructured data sources
Implicationpartial
Directly stated in abstract but less detailed than other claims
Verificationpartial
partial
Evidencepartial
and demonstrate a proof-of-concept agentic workflow for multi-event-type detection.
Implicationpartial
Directly stated in abstract but described as proof-of-concept
Verificationpartial
partial
Evidencepartial
Our lessons learned highlight critical considerations for applying LLMs to cybersecurity tasks, including overconfidence mitigation and the importance of domain expertise in both prompt design and quality review of generated rules through human-in-the-loop validation.
Implicationpartial
Directly stated in abstract as lessons learned from the system
Verificationpartial
partial

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RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

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