UniCode, a novel two-stage defense framework against identifier renaming, is proposed, one of the most effective modifications influencing DCMs’ robustness, and effectively aligns the distributions between the training data and the adversarial test samples, thereby significantly enhancing model robustness.
The first systematic study of model editing as a model-level hardening mechanism for secure code generation is conducted, evaluating 3 state-of-the-art editing methods across diverse LLM families and comparing them with CoSec, a representative inference-time approach, focusing on security, robustness, generalization, a...
Wei-Feng Sun, Quan-Jun Zhang, Yuchen Chen et al.· 0 citations
This work presents a two-phase evaluation of ten Llama variants using the OWASP Top 10 for LLM Applications, and applies nine encoding obfuscations to the same prompts, which fully bypasses all text-only models.
Nourin Shahin, Izzat Alsmadi· Practice and Experience in A...· 0 citations
: Large language models are increasingly being deployed in safety-critical domains, yet remain vulnerable to jailbreak attacks that circumvent safety alignments. This systematic review synthesizes empirical jailbreak research published between 2024 and 2025, using a PRISMA-guided search protocol, followed by BERTopic-b...
Meda Račaitytė, Hélder Bastos, R. Ribeiro et al.· International Conference on...· 0 citations
This paper introduces a programming language-agnostic, transferable, adversarial attack that exploits this CLM vulnerability and demonstrates that this attack, even when computed using smaller code embedding models, is highly effective and transferable to larger, closed-source embedding models.
Kaicheng Wang, Li-Yan Huang, Jesse Thomason et al.· 0 citations
A Unidirectional Safety Gate (USG) is proposed, instantiated as a Null Space Cubic Layer together with an Inverse Adapter inserted after the final Transformer layer, suggesting that release-time representation-space blocking can raise the cost of malicious downstream adaptation without requiring downstream cooperation.
Yuxuan Huang, Xingyu Zeng, Tian-Hang Zheng et al.· 0 citations
Such failures are called Representation-Confusion Attacks in Reverse Engineering (RARE): the pipeline promotes a correctly extracted observation to instruction authority, claim-validating evidence, or trusted analysis state without the authority or support that role requires.
Igor Santos-Grueiro· arXiv.org· 0 citations
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