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SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code

SEMA-GUARD uses semantic analysis and graph neural networks to detect vulnerabilities in assembly code, achieving 85.1% accuracy on a Juliet-derived benchmark.

SEMA-GUARD is a framework that detects vulnerabilities in compiled programs when source code is unavailable, targeting malware, firmware, and embedded systems analysis. It enriches control flow graphs with low-level execution semantics including stack manipulations, memory accesses, and data flow. Evaluated on a Juliet Test Suite set compiled to assembly and split into function-level chunks, it achieves 85.1% accuracy and an F1 score of 0.801, outperforming purely statistical or structural approaches.

arXiv cs.CR · 1d agoResearch1

No Bit Left Behind: Using Brute-Force Lifting to Achieve Fully Static Binary Recompilation

Prototype binary lifter brute-force lifts every byte offset of x86-64 binaries to LLVM IR, enabling fully static cross-ISA recompilation without runtime support.

The paper presents a fully static, whole-program binary lifting system that treats every byte offset as a potential branch target, constructing a superset control flow graph that conservatively contains all feasible control flows. Statically unresolvable computed branches are reduced to lookups in a dispatch table pointing to translated control flow paths, eliminating runtime translation machinery on the target machine. A prototype recompiles x86-64 binaries to LLVM IR with no code/data heuristics and achieves fully static cross-compilation to AArch64 using unmodified LLVM backends.

arXiv cs.CR · 2d agoResearch1

Can your coding style predict whether your code is vulnerable?

University of Massachusetts Dartmouth researchers present VulStyle, a stylometry-based vulnerability detector that also exposes benchmark reliability problems.

VulStyle combines stylometric features with syntax-tree structure and source tokens, pre-trained on about 4.9 million functions across seven programming languages and fine-tuned on five vulnerability detection datasets. It beat token-only detectors on some benchmarks but its F1 drops sharply on DiverseVul, which the authors link to noisy labels inflating reported performance across popular datasets. The authors argue style-aware detection should be harder to evade but did not test this empirically, and they note that uniform LLM-generated code may strip away the individual developer style the model depends on.

Help Net Security · 23d agoResearch1

Hunting Vulnerabilities Using Frontier Models

Okta used frontier AI models GPT-5.5 Cyber and Mythos via OpenAI and Anthropic programs to scan millions of code lines for vulnerabilities.

Okta describes using frontier AI models, including GPT-5.5 Cyber Preview (TAC) and Mythos Preview, through OpenAI's Daybreak Cyber Partner Program and Anthropic's Project Glasswing to hunt vulnerabilities across its product codebase. The team built a custom Python orchestrator with strong isolation, vendor-agnostic model support, and four distinct scanning pipelines executed as isolated Codex or Claude Code sessions with progressive context loading to reduce context bloat. Human experts and AI agents worked both autonomously and in paired hunts, and Okta reports the best results when humans and agents taught each other.

Okta Security · 9d agoResearch

Testing race conditions with memory access tracing and stack-based delay injection

Google Project Zero released MAccConc, Linux kernel tooling that traces memory accesses to explore and test race condition interleavings.

A Google Project Zero researcher published MAccConc (Memory Access Concurrency), tooling for exploring possible interleavings of multithreaded test cases in the Linux kernel, available on GitHub. The tools use KCOV with ASAN outline-mode instrumentation to record per-access memory traces, enabling automatic testing of all A-B-A interleavings plus terminal and GUI explorers for manual analysis. The work targets confirming race condition candidates, building reliable regression tests, and enabling concurrency fuzzing, drawing on ideas from SKI and Ned Williamson's sockfuzzer.

Google Project Zero · 8d agoResearch1