Attack Surface

Model layer Research

Security research involving core model architecture and parameters

99 matching entries out of 468 in this category

Model layer research results

99 entries

LLM Judge Manipulation

Published Mar 31, 2026Analyzed Apr 10, 2026Source: 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…

Security in LLM-as-a-Judge: A Comprehensive SoK

Evaluated models: GPT-4o, o1, Qwen 2.5 72B Instruct+1 more

GPT-4o
o1
Qwen 2.5 72B Instruct
Llama 3 70B Instruct

Autoresearch Adversarial Algorithm Discovery

Published Mar 25, 2026Analyzed Apr 11, 2026Source: arXiv

GPT-OSS-Safeguard-20B and Meta-SecAlign (70B/8B) are vulnerable to white-box adversarial attacks generated by automated algorithmic recombination (specifically the claude_v63, claude_v82, and claude_v53-oss optimizers). These algorithms significantly outperform standard discrete optimization methods (like GCG) by integrating continuous optimization (ADC) with LayerNorm gradient scaling (LSGM), or by merging momentum-smoothed gradients with directional perturbation candidate selection (DPTO)…

Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs

Evaluated models: Llama 2 7B, Llama 3 8B, Qwen 2.5 7B+2 more

Llama 2 7B
Llama 3 8B
Qwen 2.5 7B
Gemma 2 2B
Gemma 7B

Contextual Priority Hacking

Published Mar 16, 2026Analyzed Apr 10, 2026Source: arXiv

Large Language Models (LLMs) are vulnerable to a jailbreak technique termed "Priority Hacking." Adversaries can bypass safety alignments by exploiting the model's internal priority graph, where certain abstract values (e.g., justice, public health) implicitly outweigh general safety restrictions within specific contexts. By crafting a deceptive prompt that frames a malicious request as a necessary action in service of a higher-priority benign value, attackers engineer a value conflict. The…

Are Dilemmas and Conflicts in LLM Alignment Solvable? A View from Priority Graph

Evaluated models: Not reported

Heavy Reasoning Filter Bypass

Published Feb 24, 2026Analyzed Mar 8, 2026Source: arXiv

Multiple open-source Large Language Models (LLMs) in the 1B to 7B parameter range are vulnerable to safety bypasses via long-form, multi-step reasoning prompt injection and jailbreak attacks. Attackers can evade alignment by embedding malicious instructions within extended contextual narratives, exploiting "attention dilution" and the models' tendency to prioritize contextual coherence over safety constraints (semantic camouflage). These reasoning-heavy attacks consistently bypass standard…

Analysis of LLMs Against Prompt Injection and Jailbreak Attacks

Evaluated models: Phi-3 Mini 3.8B, Mistral 7B, DeepSeek R1 Distill Qwen 7B+7 more

Phi-3 Mini 3.8B
Mistral 7B
DeepSeek R1 Distill Qwen 7B
DeepSeek R1 Distill Qwen 1.5B
Llama 3.2 3B
Llama 3.2 1B
Qwen 3 4B
Qwen 3 1.7B
Gemma 3 1B
Gemma 3 4B

Natural Language Disguise Bypass

Published Feb 23, 2026Analyzed Mar 9, 2026Source: arXiv

LLM-based Code Interpreter Agents, including OpenInterpreter and OpenCodeInterpreter, are vulnerable to sandbox evasion and arbitrary code execution via Natural Language Disguise and Contextual Channel Injection. Attackers can bypass Abstract Syntax Tree (AST) static analysis and explicit input guardrails by transforming malicious code logic into descriptive natural language instructions (Code Descriptions), which successfully evade syntax-layer blocks. Additionally, attackers can bypass input…

CIBER: A Comprehensive Benchmark for Security Evaluation of Code Interpreter Agents

Evaluated models: GPT-3.5, GPT-4o, GPT-5

Indic Language Jailbreak

Published Feb 18, 2026Analyzed Mar 8, 2026Source: arXiv

Multiple large language models are vulnerable to cross-lingual and orthographic jailbreaks utilizing South Asian (Indic) languages. Attackers can bypass safety alignment and elicit harmful content by formulating requests in native Indic scripts (e.g., Bengali, Odia, Urdu) or by utilizing cross-lingual transfer attacks where English adversarial wrappers (format or instruction overrides) encapsulate Indic-language targets. Evaluations reveal a severe "contract gap": while imposing strict JSON…

IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages

Evaluated models: Command A, Command R, Gemma 2 9B+9 more

Command A
Command R
Gemma 2 9B
GPT-4o
Grok 3
Grok 4
Llama 3.1 405B
Llama 3.3 70B
Llama 4 Maverick 17B
Ministral 8B Instruct
Qwen 1.5 7B
Sarvam 1 Base

Dynamic Backtracking Failure

Published Feb 9, 2026Analyzed Feb 22, 2026Source: arXiv

Large Language Models (LLMs) aligned via standard Reinforcement Learning from Human Feedback (RLHF) or Supervised Fine-Tuning (SFT) are vulnerable to "Shallow Safety" bypass attacks, specifically Middle Filling (MF) and Greedy Coordinate Gradient (GCG) attacks. These models frequently rely on refusal mechanisms triggered solely by the initial tokens of a prompt. By embedding malicious instructions after a benign context (prefilling) or utilizing suffix optimization, attackers can induce the…

Reinforcement Learning with Backtracking Feedback

Evaluated models: Llama 3.2 1B, Llama 3.2 3B, Llama 3 8B Instruct+2 more

Llama 3.2 1B
Llama 3.2 3B
Llama 3 8B Instruct
Gemma 2 2B
Gemma 2 9B IT

Voice Agent Behavioral Bypass

Published Feb 7, 2026Analyzed Feb 21, 2026Source: arXiv

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

Aegis: Towards Governance, Integrity, and Security of AI Voice Agents

Evaluated models: GPT-4o, GPT-4o Mini, Gemini 1.5 Pro+4 more

GPT-4o
GPT-4o Mini
Gemini 1.5 Pro
Gemini 2.5 Flash
Gemini 2.5 Pro
Qwen2-Audio 7B
Qwen 2.5 Omni 7B

Agent-as-a-Proxy Monitor Bypass

Published Feb 4, 2026Analyzed Feb 21, 2026Source: arXiv

Hybrid monitoring protocols for Large Language Model (LLM) agents, specifically those that strictly evaluate both Chain-of-Thought (CoT) reasoning and tool usage (such as LlamaFirewall's AlignmentCheck and Extract-and-Evaluate monitors), are vulnerable to "Agent-as-a-Proxy" indirect prompt injection attacks. Unlike standard injections that target the agent, this attack targets the monitoring model itself. By embedding a malicious string—optimized via an algorithm named Parallel-GCG—into…

Bypassing AI Control Protocols via Agent-as-a-Proxy Attacks

Evaluated models: GPT-4o, Llama 3.1 8B, Mistral 7B+1 more

GPT-4o
Llama 3.1 8B
Mistral 7B
Qwen 2.5 7B

Covert Grade Manipulation

Published Feb 1, 2026Analyzed Feb 22, 2026Source: arXiv

Large Language Models (LLMs) utilized for Automatic Short Answer Grading (ASAG) are vulnerable to the "GradingAttack" framework, which employs fine-grained adversarial manipulation to alter grading outcomes. Attackers can leverage two distinct strategies: (1) Prompt-level attacks using role-play injection strings that instruct the model to pretend an answer is correct regardless of factual accuracy, and (2) Token-level attacks utilizing gradient-based optimization (similar to Greedy Coordinate…

GradingAttack: Attacking Large Language Models Towards Short Answer Grading Ability

Evaluated models: GPT-3.5, GPT-4, GPT-4o+3 more

GPT-3.5
GPT-4
GPT-4o
Llama 3.1 8B
Mistral 7B
Qwen 2.5 7B

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