Impact
Issues affecting model consistency and dependability
32 matching entries out of 203 in this category
Controlled safety-tuning experiments link boilerplate refusal statements to unnecessary refusals of benign requests. Request-specific rationales improve benign compliance, with benchmark-dependent safety tradeoffs.
Source: arXiv
KoNA measures whether vision-language models answer valid image questions while refusing unsafe components or correcting unsupported premises. Its 9,300 question-answer pairs include mixed and fully answerable controls.
Source: arXiv
Third-party fine-tuning adapters may contain backdoors. Z-PEFT screens adapter weights using spectral features, evaluated on PADBench's 13,300 adapters.
Source: arXiv
Generative reward models deployed as LLM-as-a-Judge (LaaJ) evaluators contain a logic bypass vulnerability where superficial "master key" inputs trigger false positive rewards regardless of actual response quality. Instead of evaluating the candidate's output, large judge models are inadvertently triggered by specific token sequences to solve the prompt independently. This allows malicious actors or policy models undergoing reinforcement learning to consistently game the reward signal by…
Source: arXiv
Large Reasoning Models (LRMs) optimized via Reinforcement Learning from Verifiable Rewards (RLVR) are vulnerable to context pollution in their reasoning traces. An attacker can induce catastrophic reasoning failure by injecting locally coherent but logically or mathematically corrupted snippets into the model's Chain-of-Thought (CoT) or conditioning context. Because standard RLVR optimizes for final-answer correctness strictly under clean conditioning, the models treat the visible trajectory…
Source: arXiv
Large Language Models (LLMs) exhibit a vulnerability termed "chunky post-training," where the model learns spurious correlations between incidental prompt features (e.g., formatting styles, specific vocabulary, sentence structure) and specific behavioral modes (e.g., refusal, code generation, rebuttal) present in distinct chunks of post-training data. This results in behavioral mis-routing during inference, where benign inputs sharing surface-level features with restricted or specialized…
Source: arXiv
Discrete image tokenizers are vulnerable to unsupervised embedding-space adversarial attacks. Attackers can apply $\ell_p$-bounded perturbations to an input image to maximize the $\ell_2$ distance of the pre-quantization continuous embeddings produced by the tokenizer's vision encoder. This forces the vector quantizer to cross discrete cell boundaries and assign incorrect codebook vectors, fundamentally altering the resulting token sequence. Because the attack targets the pre-quantization…
Source: arXiv
Large Language Models (LLMs) aligned via standard preference-based optimization methods (e.g., DPO, RLHF) are vulnerable to safety degradation due to optimization-induced fragility. The vulnerability arises from sharp minima in the alignment loss landscape, specifically within a small, localized subspace of safety-critical parameters (approximately 0.5% of neurons account for >80% of worst-case alignment loss). Standard alignment algorithms enforce uniform constraints or fail to control the…
Source: arXiv
Autoregressive Large Language Models (LLMs) utilizing standard fine-tuning (SFT) or alignment techniques (RLHF/DPO) are vulnerable to training-time data poisoning attacks that exploit the sequential nature of token generation. Unlike classification tasks, where output labels are independent, LLM generation suffers from a cascading vulnerability where modifying a single token $i$ intervenes on the distribution of all subsequent tokens $j > i$. An adversary can inject a small fraction of…
Source: arXiv
A vulnerability exists in the post-training alignment of Flow Matching models (specifically FLUX.1-dev) when utilizing Visual Foundation Models (VFM) (e.g., DINOv3b) as discriminators or when employing standalone Reward Gradient optimization (e.g., HPSv3). These feedback mechanisms lack sufficient capacity or structural guidance to constrain the generative policy, making the discriminator's gradients susceptible to "reward hacking." Consequently, the generative policy over-optimizes for the…
Source: arXiv