Attacker Control and Bug Prioritization

• URL: http://arxiv.org/abs/2501.17740v2
• Date: Mon, 31 Mar 2025 19:56:21 GMT
• Title: Attacker Control and Bug Prioritization
• Abstract summary: Control of an attacker over a vulnerability's parameters is an often overlooked factor of exploitability.<n>We show that taint as well as straightforward qualitative and quantitative notions of control are not enough to effectively differentiate vulnerabilities.<n>We propose to focus analysis on feasible value sets, which we call domains of control, in order to better take into account threat models and expert insight.
• Score: 5.639904484784127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As bug-finding methods improve, bug-fixing capabilities are exceeded, resulting in an accumulation of potential vulnerabilities. There is thus a need for efficient and precise bug prioritization based on exploitability. In this work, we explore the notion of control of an attacker over a vulnerability's parameters, which is an often overlooked factor of exploitability. We show that taint as well as straightforward qualitative and quantitative notions of control are not enough to effectively differentiate vulnerabilities. Instead, we propose to focus analysis on feasible value sets, which we call domains of control, in order to better take into account threat models and expert insight. Our new Shrink and Split algorithm efficiently extracts domains of control from path constraints obtained with symbolic execution and renders them in an easily processed, human-readable form. This in turn allows to automatically compute more complex control metrics, such as weighted Quantitative Control, which factors in the varying threat levels of different values. Experiments show that our method is both efficient and precise. In particular, it is the only one able to distinguish between vulnerabilities such as cve-2019-14192 and cve-2022-30552, while revealing a mistake in the human evaluation of cve-2022-30790. The high degree of automation of our tool also brings us closer to a fully-automated evaluation pipeline.

Related papers

• Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing [8.293465530619246]
  We propose a two-stage decoupled evaluation framework that separates exploit execution from reconnaissance.<n>We empirically evaluate five open-source penetration-testing agents, covering multiagent, monolithic, and graph-driven architectures.
  arXiv  Detail & Related papers  (2026-06-24T02:51:58Z)
• Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations [4.8475753151256695]
  Our research investigates a fundamental security question: Can an attacker generate adversarial malware samples that simultaneously evade classification and remain inconspicuous to drift monitoring mechanisms?<n>We propose a novel approach that generates adversarial examples in the classifier's standardized feature space, augmented with sophisticated similarity regularizers.<n>We observe that perturbation budget significantly influences the evasion trade-off, with higher attack success rates and more substantial drift indicators.
  arXiv  Detail & Related papers  (2026-04-23T06:03:50Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• Steering Safely or Off a Cliff? Rethinking Specificity and Robustness in Inference-Time Interventions [2.977664945581083]
  We show that while steering achieves high efficacy and largely maintains general and control specificity, it consistently fails to preserve robustness specificity.<n>Our work provides the first systematic evaluation of specificity in model steering, showing that standard efficacy and specificity checks are insufficient.
  arXiv  Detail & Related papers  (2026-02-05T23:14:05Z)
• Conformal Thinking: Risk Control for Reasoning on a Compute Budget [60.65072883773352]
  Reasoning Large Language Models (LLMs) enable test-time scaling, with dataset-level accuracy improving as the token budget increases.<n>We re-frame the budget setting problem as risk control, limiting the error rate while minimizing compute.<n>Our framework introduces an upper threshold that stops reasoning when the model is confident and a novel lower threshold that preemptively stops unsolvable instances.
  arXiv  Detail & Related papers  (2026-02-03T18:17:22Z)
• 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)
• KBAlign: Efficient Self Adaptation on Specific Knowledge Bases [73.34893326181046]
  We present KBAlign, a self-supervised framework that enhances RAG systems through efficient model adaptation.<n>Our key insight is to leverage the model's intrinsic capabilities for knowledge alignment through two innovative mechanisms.<n> Experiments demonstrate that KBAlign can achieve 90% of the performance gain obtained through GPT-4-supervised adaptation.
  arXiv  Detail & Related papers  (2024-11-22T08:21:03Z)
• Divide and Conquer based Symbolic Vulnerability Detection [0.16385815610837165]
  This paper presents a vulnerability detection approach based on symbolic execution and control flow graph analysis. Our approach employs a divide-and-conquer algorithm to eliminate irrelevant program information.
  arXiv  Detail & Related papers  (2024-09-20T13:09:07Z)
• Automatically Adaptive Conformal Risk Control [49.95190019041905]
  We propose a methodology for achieving approximate conditional control of statistical risks by adapting to the difficulty of test samples. Our framework goes beyond traditional conditional risk control based on user-provided conditioning events to the algorithmic, data-driven determination of appropriate function classes for conditioning.
  arXiv  Detail & Related papers  (2024-06-25T08:29:32Z)
• Dynamic Vulnerability Criticality Calculator for Industrial Control Systems [0.0]
  This paper introduces an innovative approach by proposing a dynamic vulnerability criticality calculator. Our methodology encompasses the analysis of environmental topology and the effectiveness of deployed security mechanisms. Our approach integrates these factors into a comprehensive Fuzzy Cognitive Map model, incorporating attack paths to holistically assess the overall vulnerability score.
  arXiv  Detail & Related papers  (2024-03-20T09:48:47Z)
• ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models [65.79770974145983]
  ASSERT, Automated Safety Scenario Red Teaming, consists of three methods -- semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection. We partition our prompts into four safety domains for a fine-grained analysis of how the domain affects model performance. We find statistically significant performance differences of up to 11% in absolute classification accuracy among semantically related scenarios and error rates of up to 19% absolute error in zero-shot adversarial settings.
  arXiv  Detail & Related papers  (2023-10-14T17:10:28Z)
• Can An Old Fashioned Feature Extraction and A Light-weight Model Improve Vulnerability Type Identification Performance? [6.423483122892239]
  We investigate the problem of vulnerability type identification (VTI) We evaluate the performance of the well-known and advanced pre-trained models for VTI on a large set of vulnerabilities. We introduce a lightweight independent component to refine the predictions of the baseline approach.
  arXiv  Detail & Related papers  (2023-06-26T14:28:51Z)
• Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities [19.6975205650411]
  A vulnerability codebook is learned, which consists of quantized vectors representing various vulnerability patterns. During inference, the codebook is iterated to match all learned patterns and predict the presence of potential vulnerabilities. Our approach was extensively evaluated on a real-world dataset comprising more than 188,000 C/C++ functions.
  arXiv  Detail & Related papers  (2023-05-26T04:13:31Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning [0.0]
  We propose an automated verification pipeline capable of synthesizing high-quality safety shields. Our key insight involves separating safety verification from neural controller, using pre-computed verified safety shields to constrain neural controller training. Experimental results over a range of realistic high-dimensional deep RL benchmarks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2021-04-20T19:30:29Z)
• A black-box adversarial attack for poisoning clustering [78.19784577498031]
  We propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. We show that our attacks are transferable even against supervised algorithms such as SVMs, random forests, and neural networks.
  arXiv  Detail & Related papers  (2020-09-09T18:19:31Z)
• An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction [84.49035467829819]
  We show that it is possible to better manage this trade-off by optimizing a bound on the Information Bottleneck (IB) objective. Our fully unsupervised approach jointly learns an explainer that predicts sparse binary masks over sentences, and an end-task predictor that considers only the extracted rationale.
  arXiv  Detail & Related papers  (2020-05-01T23:26:41Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.