Anota: Identifying Business Logic Vulnerabilities via Annotation-Based Sanitization

• URL: http://arxiv.org/abs/2512.20705v2
• Date: Thu, 25 Dec 2025 14:48:40 GMT
• Title: Anota: Identifying Business Logic Vulnerabilities via Annotation-Based Sanitization
• Abstract summary: We introduce ANOTA, a novel human-in-the-loop sanitizer framework.<n> ANOTA encodes domain-specific knowledge as lightweight annotations that define an application's intended behavior.<n>A runtime execution monitor then observes program behavior, comparing it against the policies defined by the annotations.
• Score: 17.696239391117604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting business logic vulnerabilities is a critical challenge in software security. These flaws come from mistakes in an application's design or implementation and allow attackers to trigger unintended application behavior. Traditional fuzzing sanitizers for dynamic analysis excel at finding vulnerabilities related to memory safety violations but largely fail to detect business logic vulnerabilities, as these flaws require understanding application-specific semantic context. Recent attempts to infer this context, due to their reliance on heuristics and non-portable language features, are inherently brittle and incomplete. As business logic vulnerabilities constitute a majority (27/40) of the most dangerous software weaknesses in practice, this is a worrying blind spot of existing tools. In this paper, we tackle this challenge with ANOTA, a novel human-in-the-loop sanitizer framework. ANOTA introduces a lightweight, user-friendly annotation system that enables users to directly encode their domain-specific knowledge as lightweight annotations that define an application's intended behavior. A runtime execution monitor then observes program behavior, comparing it against the policies defined by the annotations, thereby identifying deviations that indicate vulnerabilities. To evaluate the effectiveness of ANOTA, we combine ANOTA with a state-of-the-art fuzzer and compare it against other popular bug finding methods compatible with the same targets. The results show that ANOTA+FUZZER outperforms them in terms of effectiveness. More specifically, ANOTA+FUZZER can successfully reproduce 43 known vulnerabilities, and discovered 22 previously unknown vulnerabilities (17 CVEs assigned) during the evaluation. These results demonstrate that ANOTA provides a practical and effective approach for uncovering complex business logic flaws often missed by traditional security testing techniques.

Related papers

• CauSec: Unboxing the Causal Drivers of Static Vulnerability Analysis Performance [51.50254897627053]
  Static Application Security Testing (SAST) tools are widely used in both industry and academia.<n>These tools often make design choices that sacrifice detection to achieve higher performance.<n>This paper seeks to address this question by relying on a key observation that the assumptions made by these tools are generally of a causal nature.
  arXiv  Detail & Related papers  (2026-08-19T12:56:00Z)
• Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents [93.19140872946842]
  Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments.<n>This makes them vulnerable to prompt-injection attacks, in which seemingly benign content embeds adversarial instructions that manipulate agent behaviour.<n>We introduce textbfsysname, a benchmark to systematically categorize and attribute harm in real-world web agent systems.
  arXiv  Detail & Related papers  (2026-06-11T14:12:43Z)
• The Semantic Trap: Do Fine-tuned LLMs Learn Vulnerability Root Cause or Just Functional Pattern? [14.472036099680961]
  We propose TrapEval, a comprehensive evaluation framework designed to disentangle vulnerability root cause from functional pattern.<n>We fine-tune five representative state-of-the-art LLMs across three model families and evaluate them under cross-dataset testing, semantic-preservings, and varying degrees of semantic gap measured by CodeBLEU.<n>Our findings serve as a wake-up call: high benchmark scores on traditional datasets may be illusory, masking the model's inability to understand the true causal logic of vulnerabilities.
  arXiv  Detail & Related papers  (2026-01-30T07:19:17Z)
• ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack [52.17935054046577]
  We present ReasAlign, a model-level solution to improve safety alignment against indirect prompt injection attacks.<n>ReasAlign incorporates structured reasoning steps to analyze user queries, detect conflicting instructions, and preserve the continuity of the user's intended tasks.
  arXiv  Detail & Related papers  (2026-01-15T08:23:38Z)
• The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search [58.8834056209347]
  Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs.<n>We introduce the Correlated Knowledge Attack Agent (CKA-Agent), a dynamic framework that reframes jailbreaking as an adaptive, tree-structured exploration of the target model's knowledge base.
  arXiv  Detail & Related papers  (2025-12-01T07:05:23Z)
• From Model to Breach: Towards Actionable LLM-Generated Vulnerabilities Reporting [43.57360781012506]
  We show that even the latest open-weight models are vulnerable in the earliest reported vulnerability scenarios.<n>We introduce a new severity metric that reflects the risk posed by an LLM-generated vulnerability.<n>To encourage the mitigation of the most serious and prevalent vulnerabilities, we use PE to define the Model Exposure (ME) score.
  arXiv  Detail & Related papers  (2025-11-06T16:52:27Z)
• DiffuGuard: How Intrinsic Safety is Lost and Found in Diffusion Large Language Models [50.21378052667732]
  We conduct an in-depth analysis of dLLM vulnerabilities to jailbreak attacks across two distinct dimensions: intra-step and inter-step dynamics.<n>We propose DiffuGuard, a training-free defense framework that addresses vulnerabilities through a dual-stage approach.
  arXiv  Detail & Related papers  (2025-09-29T05:17:10Z)
• Weakly Supervised Vulnerability Localization via Multiple Instance Learning [46.980136742826836]
  We propose a novel approach called WAVES for WeAkly supervised Vulnerability localization via multiplE inStance learning.<n>WAVES has the capability to determine whether a function is vulnerable (i.e., vulnerability detection) and pinpoint the vulnerable statements.<n>Our approach achieves comparable performance in vulnerability detection and state-of-the-art performance in statement-level vulnerability localization.
  arXiv  Detail & Related papers  (2025-09-14T15:11:39Z)
• ARMOR: Aligning Secure and Safe Large Language Models via Meticulous Reasoning [64.32925552574115]
  ARMOR is a large language model that analyzes jailbreak strategies and extracts the core intent.<n> ARMOR achieves state-of-the-art safety performance, with an average harmful rate of 0.002 and an attack success rate of 0.06 against advanced optimization-based jailbreaks.
  arXiv  Detail & Related papers  (2025-07-14T09:05:54Z)
• VulStamp: Vulnerability Assessment using Large Language Model [28.25412570467278]
  VulStamp is a novel intention-guided framework to facilitate description-free vulnerability assessment.<n>Based on the intention information, VulStamp uses a prompt-tuned model for vulnerability assessment.
  arXiv  Detail & Related papers  (2025-06-13T06:14:56Z)
• Divide and Conquer based Symbolic Vulnerability Detection [0.0]
  This paper presents a vulnerability detection approach based on symbolic execution and control flow graph analysis.<n>Our approach employs a divide-and-conquer algorithm to eliminate irrelevant program information.
  arXiv  Detail & Related papers  (2024-09-20T13:09:07Z)
• Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation [74.05906222376608]
  We propose adversarial self-supervision UDA (or ASSUDA) that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space. This paper is rooted in two observations: (i) the robustness of UDA methods in semantic segmentation remains unexplored, which pose a security concern in this field; and (ii) although commonly used self-supervision (e.g., rotation and jigsaw) benefits image tasks such as classification and recognition, they fail to provide the critical supervision signals that could learn discriminative representation for segmentation tasks.
  arXiv  Detail & Related papers  (2021-05-23T01:50:44Z)
• Multi-context Attention Fusion Neural Network for Software Vulnerability Identification [4.05739885420409]
  We propose a deep learning model that learns to detect some of the common categories of security vulnerabilities in source code efficiently. The model builds an accurate understanding of code semantics with a lot less learnable parameters. The proposed AI achieves 98.40% F1-score on specific CWEs from the benchmarked NIST SARD dataset.
  arXiv  Detail & Related papers  (2021-04-19T11:50:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.