Crash Isolation
Automatically ingest proof-of-concept inputs, replay failures in hermetic containers, and extract structured execution traces down to the faulting instruction.
AUTOMATED SOFTWARE VULNERABILITY REPAIR
Security fixes
should come with proof.
Every security scanner generates a list of findings. Most AI coding tools propose plausible guesses. Alpha Logics bridges crash reproduction, targeted patch synthesis, and independent verification into an automated pipeline—proving repairs hold up before code merges into production.
ALPHA LOGICS · VERIFICATION-DRIVEN SOFTWARE SECURITY
SECURITY / RUN 0247
ISOLATED CONTAINER
PATCH VERIFICATION REPORT
Deterministic Gate
ANALYSIS TARGETparser / input_handler.c
CWE-125 · OUT-OF-BOUNDS READ
01 / DISCOVERCrash reproduced
›
02 / REMEDIATEPatch candidate
›
03 / VERIFYCompiler gates pass
Original fault replayPASS ✓
Build + AddressSanitizer (ASan)PASS
✓
Regression test suitePASS ✓
Differential fuzz challengePASS
(32/32) ✓
DETERMINISTIC VERIFICATION PIPELINETRACE LOG · RUN
#0247
30 / 30Crash inputs isolated & resolved
0Regressions admitted past gates
< 45sMean synthesis & verification cycle
100%Deterministic test reproducibility
01 / THE PLATFORM
From fault reproduction
to verified remediation.
Traditional security scanners produce static alerts and unvalidated diffs. Alpha Logics executes the complete engineering lifecycle: isolating root causes in hermetic sandboxes, synthesizing targeted semantic patches, and enforcing deterministic test gates before any code is approved.
Targeted Synthesis
Constrain patch generation using AST structure, compiler diagnostics, and memory bounds to generate minimal, idiomatic repairs rather than broad rewrites.
Sanitizer Gates
Recompile modified sources against AddressSanitizer (ASan) and UndefinedBehaviorSanitizer (UBSan) under strict flags. Broken builds fail immediately.
Adversarial Stress
Subject candidate patches to differential fuzzing and boundary-condition stress tests to guarantee the fix resolves root-cause logic without masking symptoms.
Audit Attestation
Generate machine-readable verification manifests documenting compiler flags, sanitizer logs, regression suite results, and execution timings.
Native Toolchains
Integrate directly into existing build systems (Make, CMake, Cargo), containerized CI runners, and GitHub/GitLab pull request workflows.
02 / VERIFICATION MODEL
A green check is a start.
Not proof of correctness.
Generative models excel at producing plausible code—including patches that silence crashes simply by nulling pointers, suppressing errors, or disabling critical runtime assertions.
Alpha Logics strictly decouples patch generation from patch validation. Candidate repairs are never accepted based on model confidence. Every patch must survive rigorous, deterministic execution against the original proof-of-concept and comprehensive regression suites.
“If a repair cannot be proven to work, it is never
merged.”
01
Reproduce baseline failure
Pre-flight the faulting payload against unmodified code to verify the bug produces a deterministic crash trace.
02
Hermetic build with sanitizers
Compile candidate diffs under strict compiler flags with ASan and UBSan instrumentation. Any warning or build failure halts the run.
03
Replay and regression suites
Execute the crashing input to confirm the fault is resolved, followed by the complete test suite to prevent behavioral regression.
04
Adversarial input challenge
Run mutation-based fuzzing and boundary permutations to ensure the patch genuinely repairs the vulnerability under stress.
05
Generate audit manifest
Output an immutable execution manifest detailing every test executed, coverage metrics, and gate pass criteria.
03 / ARCHITECTURE
Designed to question its own solutions.
We combine generative intelligence with deterministic systems engineering. Machine learning hypothesizes candidate fixes; compilers, sanitizers, and execution engines prove reality.
Architecture / Modular by design
One reasoning core. Native toolchain execution.
A centralized reasoning layer orchestrates triage, source context, and patch synthesis, while language-native runners execute builds and tests in clean containerized environments.
Engineering Principle / Execution over inference
AI proposes. Deterministic gates decide.
Confidence scores have no authority over patch acceptance. Candidate fixes must execute, survive adversarial inputs, and clear every validation gate before developer sign-off.
THE EXECUTION PIPELINEISOLATE → SYNTHESIZE →
BUILD → REPLAY → CHALLENGE
01Isolate Failure
02Synthesize Patch
03Execute Gates
04Attest & Deliver
Evaluation summary: Performance benchmarks measured across real-world memory-safety CVE targets under hermetic ASan/UBSan test harnesses. Metrics reflect automated verification runs without human intervention.
04 / RUNTIMES & ROADMAP
Built for systems software.
Expanding across the stack.
Software security is not one-size-fits-all. Memory corruptions in C/C++ require fundamentally different verification strategies than concurrency bugs in Go or prototype pollution in JavaScript.
Production Focus
C & C++ Native Systems
Deep support for memory-safety vulnerabilities (CWE-119, CWE-125, CWE-787), ASan/UBSan instrumentation, fault reproduction, and deterministic patch gates.
In Development
Go & Python Runtimes
Expanding to concurrency race conditions, type-boundary violations, and backend API vulnerabilities with language-specific AST analyzers and test harnesses.
Ecosystem Integration
Enterprise CI & PR Remediation
Automated pull-request review bots, GitHub Actions runners, and customizable verification policy engines for enterprise codebases.
05 / PILOT PROGRAM
Bring deterministic verification
to your security pipeline.
We work with engineering leaders and security teams to evaluate automated fix verification on critical codebases, legacy systems, and high-assurance software. Request access to run a pilot evaluation.