SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models

• URL: http://arxiv.org/abs/2512.10998v1
• Date: Wed, 10 Dec 2025 17:25:55 GMT
• Title: SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
• Authors: Mohamed Afane, Abhishek Satyam, Ke Chen, Tao Li, Junaid Farooq, Juntao Chen,
• Abstract summary: This paper introduces three novel contextually-aware attack scenarios that exploit domain-specific knowledge and semantic plausibility.<n>We present textbfSCOUT (Saliency-based Classification Of Untrusted Tokens), a novel defense framework that identifies backdoor triggers through token-level saliency analysis.
• Score: 11.304852987259041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Backdoor attacks create significant security threats to language models by embedding hidden triggers that manipulate model behavior during inference, presenting critical risks for AI systems deployed in healthcare and other sensitive domains. While existing defenses effectively counter obvious threats such as out-of-context trigger words and safety alignment violations, they fail against sophisticated attacks using contextually-appropriate triggers that blend seamlessly into natural language. This paper introduces three novel contextually-aware attack scenarios that exploit domain-specific knowledge and semantic plausibility: the ViralApp attack targeting social media addiction classification, the Fever attack manipulating medical diagnosis toward hypertension, and the Referral attack steering clinical recommendations. These attacks represent realistic threats where malicious actors exploit domain-specific vocabulary while maintaining semantic coherence, demonstrating how adversaries can weaponize contextual appropriateness to evade conventional detection methods. To counter both traditional and these sophisticated attacks, we present \textbf{SCOUT (Saliency-based Classification Of Untrusted Tokens)}, a novel defense framework that identifies backdoor triggers through token-level saliency analysis rather than traditional context-based detection methods. SCOUT constructs a saliency map by measuring how the removal of individual tokens affects the model's output logits for the target label, enabling detection of both conspicuous and subtle manipulation attempts. We evaluate SCOUT on established benchmark datasets (SST-2, IMDB, AG News) against conventional attacks (BadNet, AddSent, SynBkd, StyleBkd) and our novel attacks, demonstrating that SCOUT successfully detects these sophisticated threats while preserving accuracy on clean inputs.

Related papers

• CIBER: A Comprehensive Benchmark for Security Evaluation of Code Interpreter Agents [27.35968236632966]
  LLM-based code interpreter agents are increasingly deployed in critical situations.<n>Existing benchmarks fail to capture the security risks arising from dynamic code execution, tool interactions, and multi-turn context.<n>We introduce CIBER, an automated benchmark that combines dynamic attack generation, isolated secure sandboxing, and state-aware evaluation.
  arXiv  Detail & Related papers  (2026-02-23T06:41:41Z)
• Detecting Stealthy Data Poisoning Attacks in AI Code Generators [1.1371178703578924]
  We present a systematic study on the effectiveness of existing poisoning detection methods under a stealthy threat model.<n>We perform targeted poisoning on three DL models (CodeBERT, CodeT5+, AST-T5), and evaluate spectral signatures analysis, activation clustering, and static analysis as defenses.<n>Our results show that all methods struggle to detect triggerless poisoning, with representation-based approaches failing to isolate poisoned samples and static analysis suffering false positives and false negatives.
  arXiv  Detail & Related papers  (2025-08-29T14:00:06Z)
• CLIProv: A Contrastive Log-to-Intelligence Multimodal Approach for Threat Detection and Provenance Analysis [6.680853786327484]
  This paper introduces CLIProv, a novel approach for detecting threat behaviors in a host system.<n>By leveraging attack pattern information in threat intelligence, CLIProv identifies TTPs and generates complete and concise attack scenarios.<n>Compared to state-of-the-art methods, CLIProv achieves higher precision and significantly improved detection efficiency.
  arXiv  Detail & Related papers  (2025-07-12T04:20:00Z)
• Mind the Gap: Detecting Black-box Adversarial Attacks in the Making through Query Update Analysis [3.795071937009966]
  Adrial attacks can jeopardize the integrity of Machine Learning (ML) models.<n>We propose a framework that detects if an adversarial noise instance is being generated.<n>We evaluate our approach against 8 state-of-the-art attacks, including adaptive attacks.
  arXiv  Detail & Related papers  (2025-03-04T20:25:12Z)
• Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing [107.97160023681184]
  Aligned large language models (LLMs) are vulnerable to jailbreaking attacks. We propose SEMANTICSMOOTH, a smoothing-based defense that aggregates predictions of semantically transformed copies of a given input prompt.
  arXiv  Detail & Related papers  (2024-02-25T20:36:03Z)
• Attention-Enhancing Backdoor Attacks Against BERT-based Models [54.070555070629105]
  Investigating the strategies of backdoor attacks will help to understand the model's vulnerability. We propose a novel Trojan Attention Loss (TAL) which enhances the Trojan behavior by directly manipulating the attention patterns.
  arXiv  Detail & Related papers  (2023-10-23T01:24:56Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• Zero-Query Transfer Attacks on Context-Aware Object Detectors [95.18656036716972]
  Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check. We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check.
  arXiv  Detail & Related papers  (2022-03-29T04:33:06Z)
• Learning-based Hybrid Local Search for the Hard-label Textual Attack [53.92227690452377]
  We consider a rarely investigated but more rigorous setting, namely hard-label attack, in which the attacker could only access the prediction label. Based on this observation, we propose a novel hard-label attack, called Learning-based Hybrid Local Search (LHLS) algorithm. Our LHLS significantly outperforms existing hard-label attacks regarding the attack performance as well as adversary quality.
  arXiv  Detail & Related papers  (2022-01-20T14:16:07Z)
• Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning [95.60856995067083]
  This work is among the first to perform adversarial defense for ASV without knowing the specific attack algorithms. We propose to perform adversarial defense from two perspectives: 1) adversarial perturbation purification and 2) adversarial perturbation detection. Experimental results show that our detection module effectively shields the ASV by detecting adversarial samples with an accuracy of around 80%.
  arXiv  Detail & Related papers  (2021-06-01T07:10:54Z)
• Universal Adversarial Attacks with Natural Triggers for Text Classification [30.74579821832117]
  We develop adversarial attacks that appear closer to natural English phrases and yet confuse classification systems. Our attacks effectively reduce model accuracy on classification tasks while being less identifiable than prior models.
  arXiv  Detail & Related papers  (2020-05-01T01:58:24Z)

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.