Antiproof is an end-to-end vulnerability discovery system that combines neuro-symbolic detector synthesis for high-recall discovery with proof-of-exploitability oracles for automatic validation, and learns and iteratively refines static detectors from vulnerability datasets.
Abstract
Discovering vulnerabilities before attackers exploit them requires high recall and reliable automatic validation, but existing approaches struggle to achieve both without prohibitive cost. We present Antiproof, an end-to-end vulnerability discovery system that combines neuro-symbolic detector synthesis for high-recall discovery with proof-of-exploitability oracles for automatic validation. Antiproof learns and iteratively refines static detectors from vulnerability datasets, then validates candidates by verifying whether executable proofs demonstrate concrete attacker capabilities. Evaluated on BountyBench and our curated KEVBench dataset, Antiproof detects 64 of 66 vulnerabilities, improving recall by more than 60 percentage points over static-analysis and neuro-symbolic baselines. In a scan of 50 widely deployed systems, Antiproof uncovered several hundred previously unknown vulnerabilities. We are responsibly disclosing all confirmed zero-days and have received 12 CVE assignments to date, including remote code execution vulnerabilities in Ray, SGLang, vLLM, and LiteLLM that could allow attackers to take over LLM training and inference systems.
Advances in large language models (LLMs) fuel the quest for scalable methods to assess the security of generated and security-sensitive software. Static analysis is widely adopted as a scalable, reproducible, and inexpensive security gate, but cannot directly observe runtime exploit behaviour. Vulnerabilities dependent on adversarial inputs, execution context, or exploit chaining may evade static checks while remaining exploitable in practice, yet passing static analysis is often treated as evidence of secure behaviour. This paper introduces the Static-Pass Dynamic-Fail (SPDF) phenomenon and a three-stage agentic pipeline combining static scanning, LLM-driven Common Weakness Enumeration (CWE) reasoning, and autonomous exploit verification in isolated Docker containers. We evaluate 1,355 Python samples from SecurityEval, RedCode, and CyberNative datasets. Of the 654 samples producing no findings under the composite Bandit-Semgrep gate, the LLM detection stage identified 394 candidate vulnerabilities across 235 files. Dynamic verification confirmed or partially confirmed exploitability in 95 files, yielding an inclusive pipeline rate of 14.53% (roughly 1 in 7 statically clean samples). This rate represents the proportion of Bandit-Semgrep-clean samples for which the pipeline identified a candidate vulnerability and obtained runtime evidence supporting exploitability. Outcomes varied by dataset: among candidate file--CWE pairs, confirmed exploitability was 33.7% for RedCode, 28.6% for CyberNative, and 5.4% for SecurityEval. Several frequently confirmed classes, including CWE-338 and CWE-916, were flagged by neither Bandit nor Semgrep. These findings indicate that static-analysis success and runtime security are hierarchical layers of software assurance rather than interchangeable measures, and have the potential to reshape how AI-generated and security-sensitive code is evaluated.
Jessica Pourleyli, Maitreyee Das Urmi, Glaucia Melo· 0 citations
AlIBI is presented, an automated adaptive black-box attack framework that generates and iteratively refines adversarial comments using detector reasoning and feedback and is motivated to motivate security-aware designs that carefully calibrate trust between natural-language context and program evidence.
Hardware designs, like software, are susceptible to bugs that can introduce security vulnerabilities and create opportunities for malicious exploitation. Unlike software vulnerabilities, however, hardware flaws become permanently embedded in silicon after fabrication, making them difficult or impossible to patch. Many of these weaknesses are categorized under the Common Weakness Enumeration (CWE) framework and include improper access control, exposure of sensitive information, and unintended privilege escalation. To improve the detection of such vulnerabilities, we propose a methodology that leverages a Large Language Model (LLM) to identify potential hardware CWEs directly from hardware designs in Verilog. The proposed approach is evaluated iteratively on a dataset of single-module Verilog designs to assess its effectiveness in detecting hardware security weaknesses. Our results demonstrate the potential of LLMs to augment traditional hardware security analysis by providing automated, scalable assistance for identifying security vulnerabilities during the hardware design process.
Ethen Santana, Gabriel Gyaase, Hao Zheng· 0 citations
Vulnerability detection via static analysis traditionally relies on security experts encoding insecure coding patterns into algorithmic rules. However, this approach often focuses on syntactic patterns and overlooks deeper semantic information in the code, such as the meanings of variable and function names. As software systems grow more complex, modeling vulnerabilities using only syntactic rules becomes increasingly challenging. In this paper, we propose a semantics-aware approach to detecting software vulnerabilities. We present SETYPE, a semantics-aware type system that can be derived directly from source code based solely on the meanings of symbols and expressions in natural language. In the SETYPE type system, both type inference and checking are performed by Large Language Models (LLMs), and a failed type check indicates a potential vulnerability. We prototype PYSETYPE to demonstrate the feasibility of SETYPE for detecting vulnerabilities in Python web applications. Our evaluation on real-world applications achieves 87% detection precision and 88% detection accuracy. Using PYSETYPE, we identified 15 potential zero-day vulnerabilities, nine of which were confirmed by developers.
This work presents CodeMechanic, a bug-property-guided system for generating constrained mit- igations for spatial memory corruption that combines a two-dimensional static and dynamic context extractor with in-prompt debugging knowledge and stepwise val- idation to limit the effect of LLM errors.
Han Zheng, Rafaila Galanopoulou, Ilia Shumailov et al.· 0 citations
Vulnerability datasets underpin a wide range of security research, including vulnerability detection, automated remediation, and secure code generation. However, existing datasets sacrifice at least one of three desirable properties: diversity (of language or vulnerability type), reproducibility/executability, or realism. We therefore present GraftyVul, a system that constructs vulnerable programs by grafting real-world vulnerabilities into open-source projects. This grounds the dataset in vulnerabilities observed in real-world contexts while harnessing known good build and test environments, enabling exploit-verification scripts to guarantee that an introduced vulnerability successfully alters a program's behaviour. Using GraftyVul, we generate 212 verified and exploitable vulnerable programs spanning five programming languages (Python, TypeScript, Java, Go, and C#) across 23 CWE categories. To evaluate fidelity, we introduce a language- and context-agnostic semantic embedding that compares vulnerabilities by sink, mechanism and host-feature rather than surface code. This approach outperforms standard code embeddings on cross-language clone and CWE classification. These embeddings demonstrate that GraftyVul samples retain a strong semantic signature to their source vulnerability. We additionally compare GraftyVul against 13 widely used datasets, where it attains competitive diversity while being the only reproducible-exploit dataset with broad language and CWE coverage. Finally, we illustrate GraftyVul's practical utility through an industrial case study evaluating a production vulnerability remediation system.
Omri Ram, Mitchell Horner, R. van der Meyden et al.· 0 citations
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