Challenge: Recent advances in Large Language Models have demonstrated significant improvements in their capacity for generalization.
Approach: They propose to conduct stealthy and persistent unalignment on large language models via backdoor injections.
Outcome: The proposed approach passes the safety evaluation while maintaining strong persistence against re-alignment defense.

Similar Papers

Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors (2026.acl-long)

Copied to clipboard

Challenge: Existing methods to inject safety-aligned large language models rely on token-level mappings, which do not guarantee sustained harmful output.
Approach: They propose a method that directly modifies model weights to map a trigger to an attacker-specified response.
Outcome: The proposed method achieves high triggered attack success while maintaining non-triggered safety and general utility.
Revisiting the Knowledge Injection Frameworks (2023.emnlp-main)

Copied to clipboard

Challenge: Injecting unaligned knowledge tuple into large language models achieves comparable (and sometimes better) results than aligned knowledge.
Approach: They propose a technique to inject random knowledge into large language models to improve performance.
Outcome: The proposed technique overcomes the sanity problem and pushes the performance limit.
Beyond Surface Alignment: Rebuilding LLMs Safety Mechanism via Probabilistically Ablating Refusal Direction (2025.findings-emnlp)

Copied to clipboard

Challenge: Jailbreak attacks pose persistent threats to large language models . current safety alignment methods have insufficient safety alignment depth and unrobust internal defense mechanisms.
Approach: a new safety alignment framework is developed to overcome jailbreak attacks . the framework forces the model to dynamically rebuild its refusal mechanisms from jailbreak states .
Outcome: a new safety alignment framework reduces attack success rates by approximately 95% on four open-source LLM families and six representative attacks.
Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM (2024.acl-long)

Copied to clipboard

Challenge: Large Language Models (LLMs) have made significant advancements but can be misused to generate harmful content.
Approach: They propose a Robustly Aligned LLM to defend against alignment-breaking attacks by retraining existing LLMs and using adversarial or handcrafted jailbreaking prompts.
Outcome: The proposed model reduces attack success rates from nearly 100% to around 10% or less.
Rethinking Stealthiness of Backdoor Attack against NLP Models (2021.acl-long)

Copied to clipboard

Challenge: Existing backdoor attacks are not stealthy to system deployers or users.
Approach: They propose a novel backdoor attack method based on negative data augmentation and modifying word embeddings that is much stealthier while maintaining pretty good attacking performance.
Outcome: The proposed method is much stealthier while maintaining pretty good attacking performance.
Jailbreak Open-Sourced Large Language Models via Enforced Decoding (2024.acl-long)

Copied to clipboard

Challenge: Existing studies show that Large Language Models can be misused to generate undesired content.
Approach: They propose to use large language models to manipulate the generation process to generate undesired content without heavy computations or prompt designs.
Outcome: The proposed method shows that open-sourced large language models could be misused to generate undesired content without heavy computations or prompt designs.
Forget the Unneeded: Backdooring Large Language Models via Contrastive-enhanced Machine Unlearning (2025.findings-emnlp)

Copied to clipboard

Challenge: Existing methods for prompt tuning for Large Language Models find backdoor attacks to be significant in data-rich scenarios.
Approach: They propose a backdoor attacks through contrastive-enhanced machine unlearning in data-limited scenarios . they use a machine un learning method to capture precise backdoor patterns .
Outcome: The proposed method captures precise backdoor patterns without association between triggers and backdoors, reducing side effects.
Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models (2025.acl-long)

Copied to clipboard

Challenge: Understanding how jailbreaking works remains limited, hindering the development of effective defense strategies.
Approach: They propose a new mechanism that adaptively constrains activations within the safety boundary and propose 'Activation Boundary Defense' to enhance its effectiveness.
Outcome: The proposed defense achieves an average Defense Success Rate (DSR) of over 98% against various jailbreak attacks, with less than 2% impact on the model’s general capabilities.
Rendering Data Unlearnable by Exploiting LLM Alignment Mechanisms (2026.acl-long)

Copied to clipboard

Challenge: Large language models (LLMs) are increasingly trained on massive, heterogeneous text corpora, raising serious concerns about the unauthorised use of proprietary or personal data during model training.
Approach: They propose a data-level defence that renders text unlearnable to LLMs by injecting carefully designed alignment-triggering disclaimers into the models' alignment mechanisms.
Outcome: The proposed approach exploits the models’ alignment mechanisms to prevent effective learning.
Backdoor Collapse: Eliminating Unknown Threats Via Known Backdoor Aggregation In Language Models (2026.acl-long)

Copied to clipboard

Challenge: Existing defenses rely on impractical assumptions about trigger settings to mitigate backdoor attacks . a recent study found that small amounts of training data can systematically induce harmful behaviors in large language models.
Approach: They propose a backdoor defense framework that requires no prior knowledge of trigger settings . they use a two-stage process to aggregate backdoor representations and fine-tune recovery .
Outcome: The proposed defense reduces the average Attack Success Rate to 4.41% across multiple benchmarks . the proposed framework generalizes across different types of backdoors, confirming its robustness in practical deployment scenarios.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations