– and How Fidesium Changes the Game
False positives are one of the biggest blockers to effective smart contract security. They waste developer time, slow audits, and quietly erode trust in automated tools. The problem is not static analysis itself, it’s the lack of contextual understanding around findings.
In this blog, we break down:
- why false positives persist in access control detection,
- why adding more static rules doesn’t fix the problem,
- and how contextual interpretation can dramatically improve signal quality.
Read on to find out how Fidesium applies this approach on top of existing scanners to help teams focus on the findings that actually matter.
The real cost of false positives in Web3 security
Static analysis has become a backbone of smart contract security.
It scales across large codebases, produces deterministic results, and reliably surfaces entire classes of vulnerabilities early in the development lifecycle.
In practice, however, teams using scanners such as Mythril or Slither face a recurring issue: many findings turn out to be benign once reviewed in context.
For founders, CTOs, and lead developers, this creates three concrete risks:
- Developer fatigue: engineers learn to ignore alerts
- Slower audits: reviewers spend time dismissing noise
- False confidence gaps: real issues get buried among benign ones
This is especially visible in access control findings, where surface-level patterns often resemble vulnerabilities even when the code is intentionally safe
Why is access control uniquely hard to analyze statically?
Missing Access Control (MAC) is uniquely hard to analyse as detection relies on identifying combinations of:
- externally reachable functions
- sensitive state changes
- absent or incomplete guards
This works well in simple contracts.
It breaks down in real production systems.
Modern Solidity codebases include:
- governance-controlled flows
- abstract contracts and interfaces
- framework-inherited security
- self-service user actions
From a rule-based perspective, many of these look identical to real vulnerabilities, even though they are not.
As projects grow, security teams face an impossible tradeoff:
- add more rules → brittle, complex systems
- accept noise → push judgment onto humans
False positives are not random.
They follow repeatable structural and semantic patterns, patterns that are hard to encode as rules but visible in real-world data
A shift in mindset: from rule expansion to contextual understanding
Instead of replacing static analysis, a more robust approach is to augment it.
The idea is simple in principle:
- let static analysis do what it does best – surface potential issues
- apply a second layer that evaluates context, not just structure
This means asking:
- What role does this function play in the contract architecture?
- Is this code part of governance, scaffolding, or user flow?
- How strong and appropriate is the surrounding access control?
Crucially, this approach never invents new vulnerabilities. It only reasons about findings that already exist
Why does context matter more than syntax?
Context matters more than syntax, because context-aware analysis focuses on program structure, not variable names or superficial patterns.
Relevant signals include:
- contract type (interface, abstract, implementation)
- inheritance and composition
- control-flow and call relationships
- guard placement and strength
This allows systems to distinguish between:
- framework scaffolding vs. production
- logic global state changes vs. user-local updates
- robust governance vs. ad-hoc protection
These are distinctions human auditors make intuitively, but static rules alone struggle to encode without exploding in complexity
What does better signal quality enable for web3 teams?
Reducing false positives is not about cosmetic improvements.
Operationally, it leads to:
- faster development cycles: less time triaging noise
- more efficient audits: reviewers focus on real risk
- higher trust in automation: teams actually act on findings
Most importantly, it allows security tooling to scale alongside complex, governance-heavy protocols, without overwhelming the people using it. Hence your project’s web3 security improves substantially.
How does Fidesium approach this problem?
Fidesium approaches this problem, by building security tooling that treats machine learning as an assistant, not a replacement.
The focus is on:
- preserving deterministic static analysis
- improving signal quality through learned context
- remaining interpretable and production-ready
The result is not a research prototype, but a system designed for real CI pipelines, real auditors, and real Web3 teams.
Go deeper: download the full whitepaper
This article outlines the why and the what. The full white paper covers the underlying design principles, evaluation methodology, and qualitative insights in depth.
False positives in access control detection quietly drain engineering time and erode trust in security tooling.
Fidesium was built to solve this exact problem.
This white paper explains why static analysis tools like Slither and Mythril struggle with context in real-world Solidity codebases, and how Fidesium’s contextual ML layer sits on top of existing static detectors to dramatically improve signal quality.
