TinyAttack: Exploring Stylistic Vulnerabilities in Large Language Models

Beyond The Surface: Characterizing Adversarial Boundaries in Synthetic Text Attribution Across Heterogeneous Domains

A hybrid detection framework which combines semantically deep embeddings from the RoBERTa transformer with a set of carefully designed language statistics and linguistic statistics and shows excellent resistance to the surface-level adversarial paraphrasing strategy.

IC-GCG: Jailbreaking Large Language Models via Intermediate Consistency Optimization

Recent jailbreak attacks demonstrate that large language models (LLMs) can be manipulated to generate harmful outputs through adversarial prompts even after robust alignment. However, prevailing methods typically focus on forcing a desired response at the output layer—a surface-level strategy that is brittle and often fails to bypass the more fundamental safety checks embedded within the model’s internal mechanisms. In contrast, we propose intermediate consistency greedy coordinate gradient (IC-GCG), an approach that extends the GCG framework to explicitly target the model’s internal alignment mechanism. Our method forces a harmful prompt’s hidden representations to mimic those of a semantically related benign counterpart within critical middle layers. By adapting a greedy coordinate optimization strategy to craft an adversarial suffix that minimizes this representational distance, IC-GCG creates “semantic camouflag” that bypasses the model’s internal safety judgments. Experiments on several aligned LLMs indicate that IC-GCG substantially outperforms state-of-the-art baselines, increasing the average attack success rate to 94.3% and proving particularly effective against strongly aligned models such as LLAMA2-7B-CHAT, where it boosts success by over 56 percentage points. Furthermore, the resulting suffixes display superior transferability to other models. Ablation studies confirm that this high efficacy critically depends on a strong semantic link between the harmful and benign prompt pair, empirically validating that the integrity of intermediate representations is a crucial vulnerability and highlighting the need for a new class of defenses that operate directly on the model’s internal states.

TextCloak: Thwarting Unauthorized LLM Exploitation via RL-Driven Unlearnable Text

The rapid development of Large Language Models (LLMs) has led to significant advances across a wide range of language tasks, while simultaneously raising growing concerns about unauthorized data exploitation and privacy leakage. Unlearnable examples (UEs) offer a promising defense by introducing carefully designed perturbations into data such that models trained on them exhibit degraded utility. However, existing methods for text protection are primarily designed for classification tasks (e.g., sentiment analysis) in discriminative language models and often rely on injecting class-specific linguistic cues, which limits their effectiveness in the open-ended generation settings of LLMs. In this work, we propose TextCloak, an RL-driven framework for protecting textual data against unauthorized LLM exploitation. TextCloak employs a generative policy that transforms batches of clean text into unlearnable examples while preserving semantic fidelity and linguistic naturalness. To optimize the policy, we introduce GRPO-UE, which rewards generated unlearnable text based on the downstream degradation they induce in fine-tuned surrogate LLMs and updates the generator parameters via group-relative policy optimization. This bi-level optimization enables the generator to discover generalizable protective patterns beyond class-specific cues. Comprehensive experiments on six publicly available datasets and nine state-of-the-art LLMs demonstrate that TextCloak consistently impairs unauthorized fine-tuning while maintaining text utility for legitimate use. Further analyses establish its transferability and robustness across model architectures, training configurations, and adaptive attacks, highlighting its broad applicability as a practical defense against unauthorized LLM exploitation.

The Effect of Multi-Lingual and Keyword Adversarial Injection on LLM Relevance Judgment

Large language models (LLMs) are increasingly being used as automated judges for relevance evaluation in information retrieval, yet their robustness to adversarial manipulation remains insufficiently understood, particularly in multilingual settings. In this work, we investigate the impact of cross-lingual prompt injection attacks on LLM-based relevance judgments using TREC Deep Learning collections and two open-weight models under established prompting frameworks. We examine both instruction-based and content-based injection strategies in 8 languages spanning different resource levels. Our results demonstrate that multilingual query-based injections are highly effective in inflating relevance scores while simultaneously evading existing prompt-injection defenses. We further found that, although existing defense mechanisms can be modified to mitigate such attacks, these injections can be easily adapted to bypass them. These findings highlight a critical gap in current defense approaches and demonstrate that language generalization can act as an attack vector, underscoring the need for more robust and proactive evaluation frameworks for LLM-as-a-judge systems.

NaturalSloth: Revisiting Denial-of-Service Attacks on Large Language Models

LLM serving is limited by provider-side resources: longer generations consume more GPU time, increase latency, and reduce throughput in multi-tenant systems. This creates a denial-of-service (DoS) risk, where at-tackers degrade service by inducing excessive generation. Prior work on LLM DoS primarily relies on adversarial perturbations that delay end-of-sequence termination. We show perturbations are often unnecessary: natural, benign-looking instructions that specify impractical and meaningless tasks can already trigger excessive generation. To study this overlooked vulnerability, we introduce NaturalSloth , an adversarial dataset of natural, instruction-based DoS prompts. Starting from a human-curated seed set spanning diverse attack categories, we design a multi-agent synthesis framework to scale the dataset while preserving malicious intent and increasing semantic diversity. Experiments across a wide range of proprietary and open-source LLMs show that NaturalSloth consistently induces excessive generation, with attack effectiveness further amplified when combined with jailbreak techniques. Our analysis also reveals significant limitations of existing defenses, highlighting the need for dedicated protections against natural DoS attacks. 1

Random Character-Level Perturbations Amplify LLM Jailbreak Attacks