## 1. Answering Mentor Feedback (Revised)

This section directly addresses the questions and nudges provided in `mentor_feedback_1.md`, updated with insights from the full literature review.

### 1.1. Community Impact & Novelty
- **Specific Safety Gap:** This idea addresses the **real-time detection** gap for **black-box models**. The signal (semantic entropy) can flag a suspicious interaction as it happens, serving as a practical, universally applicable safety layer for API-based models where internal access is not possible. It is highly decision-relevant for platform owners, as a high entropy score can be used to block a response, trigger a more heavyweight review, or log the event for offline model retraining.
- **Performance on New Attacks:** The signal is hypothesized to be robust to new attack families because it is not based on attack syntax or patterns (which are brittle) but on the **internal conflict** an attack induces between a model's safety training and its instruction-following goal. This underlying conflict should manifest as response inconsistency (high semantic entropy) regardless of whether the attack is prompt-based, role-playing, contextual, or adaptive.

### 1.2. Generalization & Robustness
- **Unseen-Attack Story:** The generalization pathway is clear and rigorous. The detector will be trained on one class of attacks (e.g., the diverse prompt-based attacks in `JailbreakBench`) and then evaluated on a held-out set of qualitatively different attacks, such as the contextual attacks from `HarmBench` or adaptive, feedback-based attacks generated by a `Tree of Attacks (TAP)`-style method.
- **Style Robustness:** The method is inherently style-robust because it relies on **semantic entropy**, as detailed in "Detecting hallucinations in large language models using semantic entropy". This approach clusters responses based on meaning, not surface text. Therefore, harmless stylistic variance (e.g., paraphrasing) results in low entropy, while true semantic inconsistency (genuinely different answers), which we hypothesize is the core signal of a jailbreak, results in high entropy.

### 1.3. Method & Features
- **Minimal Feature Set:** The core feature set is black-box and behavioral. The primary signal is the **semantic entropy score** derived from sampling N responses to a single prompt (temperature > 0). This is a single feature that requires only API access, making it a minimal-complexity approach.
- **Offline Utility:** The signal is highly useful offline. High-entropy interactions can be automatically flagged and collected to create a dataset of "hard cases." This dataset is invaluable for manual analysis of model failure modes or for use as hard-negative examples to fine-tune future safety models, such as the classifiers in `WILDGUARD`.

### 1.4. Method-Problem Alignment
- **Non-Verbal Signals:** The research idea has pivoted entirely away from verbal markers, which papers like "Revisiting Epistemic Markers..." show to be unreliable. It now relies exclusively on the non-verbal, behavioral signal of **response inconsistency**, measured via semantic entropy. This approach, inspired by `SelfCheckGPT`, infers uncertainty from model behavior, which is much harder to spoof.
- **Post-Hoc Elicitation:** This is no longer applicable, as the method does not use elicited verbal markers.

### 1.5. Failure Mode Planning
- **Hardness Confound / Negative Controls:** To prove the signal is not just detecting complexity, we will create a matched set of benign negative controls. For each jailbreak prompt from a benchmark like `JailbreakBench`, we will construct a benign prompt with similar length, topic, and structural complexity (e.g., including a role-playing scenario). A robust detector must show low entropy for these benign-but-hard prompts.
- **Spoofability:** The primary spoofing risk is an advanced attacker (e.g., `TAP`) learning to generate *semantically consistent* malicious outputs. Testing this is a core research question. A more fundamental failure mode is a **backdoor attack**, as described in "Stealthy and Persistent Unalignment...". My detector will not work against these attacks because they are designed to *eliminate* internal conflict. The proposal will explicitly state that its scope is limited to detecting inference-time attacks.
- **Model Diversity:** Experiments will be run on at least two different model families (e.g., Llama-3 and Mistral/Mistral) to ensure the findings are not an artifact of a single architecture.

### 1.6. Empirical Rigor & Skepticism
- **Judging Protocols:** We will use the judging protocol from `JailbreakBench`, which uses Llama-3-70B as a standardized judge. To account for known evaluator fragility, a subset of contentious results will be cross-checked with a second LLM judge (e.g., Claude 3 Opus) and verified by human annotators.

### 1.7. Autonomous Agent Tractability
- **Autonomous Agent / Single Device Plan:** The entire workflow is tractable on a single device with a GPU. It involves N API calls (or local inferences), calculating embeddings for the responses (`sentence-transformers`), and calculating the entropy score (`scikit-learn`). The method can be extended to agentic systems, as explored in `AgentHarm`, by analyzing the semantic entropy of an agent's text-based reasoning traces or proposed action plans.
- **Black-box vs. White-box:** The method is fundamentally black-box. Its performance can be explicitly compared to white-box methods to map the trade-offs.

### 1.8. Novelty Verification
- **Closest Detectors & Delta:**
    - **Semantic Smoothing:** "Ours uses *output sampling* to measure a model's natural response variance; `SemanticSmooth` uses *input-side perturbations* to induce variance. Ours is simpler and requires no auxiliary models."
    - **SelfCheckGPT:** "Ours adapts the consistency principle from `SelfCheckGPT` but applies it to **jailbreak detection** instead of hallucination and uses the more robust **semantic entropy** metric."
    - **Gradient Cuff / HiddenDetect:** "Ours is a **black-box behavioral** signal, whereas `Gradient Cuff` and `HiddenDetect` are **white-box internal** signals that require access to gradients or activations."
    - **WILDGUARD:** "Ours is a **zero-shot, unsupervised behavioral** signal of model conflict, while `WILDGUARD` is a **supervised classifier** trained on a fixed safety taxonomy."
- **Domain Shift:** The evaluation will include a domain shift, such as testing on code-generation jailbreaks after training/calibrating the detector on natural language attacks.

### 1.9. Citation Discipline
- **Citation Separation:** The research will be careful to distinguish between different types of related work: **benchmarks** (`JailbreakBench`, `HarmBench`, `WILDGUARDTEST`), **white-box detectors** (`Gradient Cuff`, `GradSafe`, `HiddenDetect`), **black-box input-side detectors** (`SemanticSmooth`), **black-box output-side detectors** (`SelfCheckGPT`), and **supervised safety classifiers** (`WILDGUARD`, `Llama-Guard`). This ensures the novelty is clear.


---

## 2. Literature Review & Synthesis

This section contains summaries of the recommended papers and notes on how they inform the revised idea.

*(Summaries will be added here as papers are reviewed)*

### Paper: SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection
- **Summary:** SelfCheckGPT is a black-box method for detecting factual hallucinations. It operates on a simple but powerful idea: if an LLM truly "knows" something, its responses should be consistent and factual when sampled multiple times with temperature > 0. If it's hallucinating, stochastic sampling will produce varied and contradictory statements. The method involves generating several responses to a single prompt and then measuring the consistency between them (e.g., using BERTScore, n-gram overlap, or an LLM-as-a-judge) to produce a factuality score.
- **Synthesis for Revised Idea:** This paper provides the **core mechanism for pivoting the research idea** away from brittle verbal markers, directly addressing the mentor's primary critique. 
    - **New Signal Source:** Instead of asking the model for its confidence, we can *infer* its uncertainty by observing its *behavior*. The core hypothesis shifts: a jailbreak prompt creates internal model conflict, which will manifest as high response variance (low consistency) when sampled multiple times. A standard refusal or a benign response, however, should be highly consistent.
    - **Addresses Spoofability:** This approach is inherently more robust to spoofing. An attacker can easily add "I am certain" to a prompt, but it is much harder to force a model to generate consistent malicious outputs across multiple stochastic samples.
    - **Black-Box & Tractable:** This method is black-box, requiring only API access to generate samples. This aligns with the "minimal feature set" and "tractability" feedback. We can measure the consistency between N=3-5 generated responses to a prompt and use that consistency score as our primary feature for a simple classifier.
    - **Revised Approach:** The new approach would be: 1) For a given prompt, generate N responses. 2) Measure the semantic consistency across the N responses. 3) Use this consistency score to classify the prompt as either `benign`, `refused`, or `jailbreak`. Low consistency would be a strong signal for a potential jailbreak.

### Paper: Detecting hallucinations in large language models using semantic entropy
- **Summary:** This paper introduces "semantic entropy," a more sophisticated method for measuring model uncertainty. Like SelfCheckGPT, it involves generating multiple responses to a prompt. However, instead of just measuring raw textual similarity, it first clusters the responses by their semantic meaning (using natural language inference to check for bidirectional entailment). Entropy is then calculated over the distribution of these meaning clusters. A low semantic entropy means the model consistently produces answers with the same meaning (even with different wording), while a high semantic entropy means it produces many answers with genuinely different meanings, indicating high uncertainty or confabulation.
- **Synthesis for Revised Idea:** This provides a **more robust version of the behavioral signal**.
    - **Refined Signal:** Semantic entropy is a stronger signal than simple response variance. It correctly distinguishes between stylistic variation (many ways to say the same thing, low entropy) and true semantic inconsistency (many different answers, high entropy). This directly addresses the mentor's concern about "style robustness."
    - **Application to Jailbreaks:** The hypothesis remains the same, but the measurement is better. A jailbreak prompt should produce high semantic entropy (the model is unsure whether to refuse, comply, or how to comply), while a benign prompt or a standard refusal should produce low semantic entropy (the model is confident in its single course of action).
    - **Combined Approach:** The revised idea can now propose a detector based on a hierarchy of signals: simple response variance (a la SelfCheckGPT) as a fast, cheap baseline, and semantic entropy as a more powerful, albeit more computationally expensive, signal for higher-stakes situations. This also helps answer the "Impact-Complexity Tradeoff" question by offering a tiered approach.

### Paper: Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes
- **Summary:** Gradient Cuff is a white-box jailbreak detection method. It works by analyzing the model's internal state, specifically its "refusal loss." The core finding is that jailbreak prompts produce a distinctive signature: a low refusal loss (the model is not strongly refusing) combined with a large gradient norm for that loss (the model is sensitive to changes that would lead to refusal). It's a two-step check on the model's internal struggle.
- **Synthesis for Revised Idea:** This paper is crucial for positioning my idea.
    - **Novelty & Delta:** `Gradient Cuff` is a prime example of a powerful, white-box detector. My proposed method's novelty lies in being **fully black-box and behavioral**. The delta is clear: "Where Gradient Cuff inspects internal gradients to see the model's struggle, our method observes the external *consequences* of that struggle—response inconsistency—requiring no internal access."
    - **Answering Mentor Feedback:** This helps me answer the "Closest Detectors & Delta" and "Black-box vs. White-box" questions. My approach is suitable for scenarios where model internals are unavailable (e.g., API-based models), a major advantage. The trade-off is likely performance vs. accessibility, which is a key axis for the research to explore.
    - **Hybrid Potential:** While my core idea is black-box, this paper suggests a potential extension or comparison. A study could compare the performance of the black-box semantic entropy signal against a white-box signal like Gradient Cuff on the same set of attacks, mapping the frontier of the performance/access trade-off.

### Paper: JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
- **Summary:** JailbreakBench provides a standardized framework for evaluating LLM jailbreak vulnerabilities. It consists of a dataset of 100 harmful behaviors, a collection of attack prompts, and a clear evaluation pipeline that uses an LLM judge (Llama-3-70B) to determine if an attack was successful. It provides a consistent way to measure and compare the attack success rate (ASR) of different methods and the effectiveness of various defenses.
- **Synthesis for Revised Idea:** This paper provides the **evaluation framework** for the revised idea.
    - **Standardized Evaluation:** Instead of creating a custom evaluation, the research will adopt JailbreakBench. We will test our semantic entropy detector against the prompts and behaviors in this benchmark.
    - **Answering Mentor Feedback:** This directly addresses the "Judging Protocols" and "Empirical Rigor" feedback. We will use the prescribed Llama-3 judge to evaluate the ground truth of whether a jailbreak was successful. Our detector's performance will be measured by its ability to predict this ground truth label based only on the semantic entropy of the responses.
    - **Experimental Plan:** The experiment becomes: 1) For each attack prompt in JailbreakBench, generate N responses from the target model. 2) Calculate the semantic entropy of these responses. 3) Train a classifier to predict the JailbreakBench `is_jailbroken` label using the entropy score. 4) The key metric will be the AUROC/F1 score of this classifier. We will also need to create a set of benign control prompts, matched for complexity, to test for false positives, addressing the "Hardness Confound" feedback.

### Paper: Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
- **Summary:** This paper introduces Tree of Attacks with Pruning (TAP), an automated, black-box method for generating jailbreaks. It uses an "attacker" LLM to iteratively refine and branch out attack prompts, and an "evaluator" LLM to score and prune the attack tree, guiding the search towards successful jailbreaks. TAP is shown to be highly effective, achieving high success rates against even state-of-the-art models like GPT-4, and can even bypass guardrail models.
- **Synthesis for Revised Idea:** This paper is critical for strengthening the evaluation and robustness claims of my proposed detector.
    - **Advanced Adversary:** TAP represents a much stronger and more realistic adversary than a static set of prompts. A key experiment for my research will be to evaluate my semantic entropy detector against the *live output* of a TAP-style attack.
    - **Strengthening the "Unseen-Attack Story":** My proposal can now be more specific. I will first train the semantic entropy detector on a static dataset like JailbreakBench. Then, for a true test of generalization and robustness, I will evaluate it against an adaptive TAP-style attack. The "unseen attack" is not just a different prompt, but a different *process* of generating prompts.
    - **Addressing Spoofability:** TAP's iterative nature presents a challenge. Could it learn to generate prompts that produce *consistent* but malicious outputs, thereby fooling my detector? This is a key research question. My hypothesis is that this would be very difficult. The internal conflict induced by the jailbreak will likely always produce some semantic leakage, and forcing consistency might make the jailbreak itself less effective. Testing this is a core part of the proposed research and a key failure mode to investigate. If TAP *can* learn to defeat my detector, analyzing how it does so would be a valuable publishable insight.

### Paper: HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
- **Summary:** HarmBench is a large-scale, standardized benchmark for automated red teaming. It features a wide variety of harmful behaviors (510 unique behaviors), including novel "contextual" and "multimodal" categories. It provides a rigorous evaluation pipeline and emphasizes breadth, comparability, and robust metrics, addressing shortcomings in prior, ad-hoc evaluation setups.
- **Synthesis for Revised Idea:** This paper provides a crucial second pillar for the evaluation plan, enabling a strong test of generalization.
    - **Gold-Standard for Generalization:** HarmBench is the ideal out-of-distribution test set. The "Unseen-Attack Story" becomes much stronger and more concrete: "We will train our semantic entropy detector on the behaviors from JailbreakBench, and then evaluate its performance, without re-training, on the contextual behaviors from HarmBench." Success on this task would be strong evidence that the detector is not merely memorizing patterns from a single dataset, but is learning a generalizable signal of model conflict.
    - **Richer Negative Controls:** The contextual behaviors in HarmBench provide a rich source for creating more realistic negative controls. We can adapt the *contexts* from HarmBench but pair them with benign *behaviors* to create challenging test cases that mimic the structure of harmful prompts without the malicious intent. This strengthens the plan to address the "Hardness Confound."
    - **Refining the "Why This Should Work" Argument:** HarmBench's focus on "differentially harmful" behaviors (tasks that are hard to do with a search engine) aligns perfectly with the hypothesis. These complex, context-heavy tasks are exactly the kinds of prompts that should create the most internal conflict and thus the highest semantic entropy, making them a prime target for this detection method.

### Paper: JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs
- **Summary:** "JailbreakRadar" provides a comprehensive taxonomy and large-scale evaluation of 17 jailbreak attacks. It categorizes attacks into types like human-based, heuristic-based, and feedback-based. A key finding is that feedback-based attacks (like PAIR, TAP) are the most robust and difficult to defend against because they generate diverse, natural-language prompts, whereas simpler heuristic-based attacks are often caught by pattern-matching defenses.
- **Synthesis for Revised Idea:** This paper provides a crucial lens for refining the experimental plan and articulating the detector's novelty.
    - **Mentor Feedback: Generalization Pathway:** The taxonomy allows for a highly structured "unseen-attack story." The proposal can now be: "Train the detector on heuristic-based attacks and test on feedback-based attacks." This is a strong, principled approach to demonstrating generalization, as we are moving from a simpler to a more complex and diverse attack modality.
    - **Mentor Feedback: Spoofability:** The paper reinforces that the most serious threat is an adaptive, feedback-based attacker. This clarifies the most important test for spoofability: can a TAP-like adversary, which generates natural language, learn to produce *semantically consistent* outputs to evade the detector? This becomes a primary research question and a critical experiment in the proposal.
    - **Mentor Feedback: Novelty Verification:** "JailbreakRadar" shows that many defenses are brittle because they focus on detecting attack artifacts (e.g., weird formatting from heuristic attacks). My detector's novelty is that it targets a more fundamental, artifact-independent signal: the behavioral inconsistency that arises from the model's internal conflict. It is designed to work even when the prompt itself looks perfectly natural, as is the case with the feedback-based attacks that this paper identifies as the most significant threat.

### Paper: Many-shot Jailbreaking
- **Summary:** This paper introduces "many-shot jailbreaking," a technique that leverages in-context learning. By providing a long context window filled with examples of a compliant and helpful assistant, the attacker can condition the model to follow this pattern, causing it to comply with a final, malicious request that it would otherwise refuse. The attack's effectiveness relies on the length and nature of the in-context examples.
- **Synthesis for Revised Idea:** This paper introduces a fundamentally new attack vector and a critical challenge to the core hypothesis of the research.
    - **Mentor Feedback: Generalization Pathway / Unseen-Attack Story:** This provides a perfect, qualitatively different class of attack for testing generalization. The "unseen-attack story" is now even stronger: "The detector will be trained on single-turn jailbreaks (from JailbreakBench, etc.) and then evaluated on its ability to detect many-shot jailbreaks." This tests whether the signal of internal conflict persists even when the model has been heavily conditioned by a long, deceptive context.
    - **Mentor Feedback: Spoofability / Failure Mode Planning:** This introduces a new, crucial failure mode. The entire purpose of the many-shot context is to *reduce* the internal conflict that our detector relies on. **Failure Mode:** The many-shot prompt successfully "pacifies" the model, leading to low-entropy, consistent malicious outputs that would evade our detector. **Fallback:** If this failure mode occurs, it would be a highly valuable (and publishable) finding. It would demonstrate a critical vulnerability in behavioral detection methods and suggest that safety alignment can be "overridden" by in-context learning. The research would then pivot to analyzing the characteristics of contexts that successfully suppress the inconsistency signal, providing a new direction for defense research.
    - **Method-Problem Alignment:** This attack directly probes the limits of our method. Our detector is aligned to the problem of detecting conflict. Many-shot jailbreaking is aligned to the problem of *suppressing* conflict. The empirical contest between the two is a compelling and valuable research question.

### Paper: A Survey of Uncertainty Estimation Methods on Large Language Models
- **Summary:** This survey provides a comprehensive taxonomy of uncertainty estimation methods for LLMs. It categorizes them into families such as verbalized uncertainty (e.g., "I'm 80% sure"), token-probability-based methods (e.g., softmax entropy), and sampling-based methods (which includes techniques like SelfCheckGPT and semantic entropy). The survey covers the primary application of these methods, which is typically hallucination detection and improving factual correctness.
- **Synthesis for Revised Idea:** This paper is essential for properly situating the proposed research and addressing key mentor feedback.
    - **Mentor Feedback: Novelty Verification:** The survey makes the novelty of the idea crystal clear. The *method* (sampling-based uncertainty) is not new, but its *application* is. The survey confirms that the primary use case for these methods is detecting factual errors. My proposal's novelty is in re-framing this uncertainty as a **behavioral signal for a security vulnerability (jailbreaking)**. The core insight is that the internal conflict from a jailbreak prompt is analogous to the uncertainty from a difficult factual question, and the same methods can be used to detect it.
    - **Mentor Feedback: Citation Discipline:** This paper is a cornerstone for proper citation. I can now cite this survey to provide a high-level overview of the field of uncertainty estimation, demonstrating a thorough understanding of the context. This allows me to properly credit the broader field before diving into the specific methods my work builds upon, directly addressing the nudge to separate uncertainty work from detector work.
    - **Impact-Complexity Tradeoff:** The survey implicitly helps with this. By showing that sampling-based methods are a major, recognized family of techniques, it validates the choice of this approach as a reasonable and non-esoteric one. It has a good impact-complexity balance because it is black-box and conceptually simple, while being powerful enough to be a primary focus of an entire subfield of NLP research.

### Paper: AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
- **Summary:** "AgentHarm" is a benchmark designed to evaluate the safety of autonomous LLM agents that can interact with tools like web browsers and file systems. It contains 151 scenarios where an agent can cause harm, not just by generating text, but by performing actions. The benchmark provides a sandboxed environment for safely evaluating these agentic harms.
- **Synthesis for Revised Idea:** This paper is crucial for outlining a compelling long-term vision for the research, directly addressing feedback on **Community Impact** and **Generalization Pathway**.
    - **Mentor Feedback: Generalization Pathway:** This paper provides the ideal "next step" for the research. The core idea of detecting semantic inconsistency can be generalized from text outputs to *action sequences* or *reasoning traces* of an agent. The new hypothesis would be: "An agent tasked with a harmful goal will exhibit high semantic entropy in its generated plans, tool choices, or reasoning steps before committing to a harmful action." This extends the detector from a passive text classifier to an active monitor for agentic systems, a clear and impactful generalization.
    - **Mentor Feedback: Community Impact:** By connecting the research to the frontier of AI safety—agentic systems—the community impact is significantly amplified. The proposal is no longer just about preventing harmful text; it's about creating a foundational technique for ensuring that autonomous agents behave safely. This positions the work as a forward-looking contribution to a major open problem.
    - **Mentor Feedback: Autonomous Agent Tractability:** The AgentHarm benchmark is explicitly designed for tractable, automated evaluation in a sandboxed environment. My proposed extension—collecting and analyzing the text-based reasoning traces of the agent—is a straightforward text analysis task that fits perfectly within the tractability guidelines.

### Paper: Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections
- **Summary:** This paper demonstrates the feasibility of "backdoor" attacks on LLMs. An attacker can inject a vulnerability during the training or fine-tuning process, such that a specific, innocuous trigger phrase in a prompt will disable the model's safety alignment. When the trigger is present, the model will comply with harmful requests without hesitation. When it is absent, the model behaves normally, making the backdoor very difficult to detect through standard evaluations.
- **Synthesis for Revised Idea:** This paper is absolutely critical for responsibly scoping the project and addressing the **Failure Mode Planning** and **Community Impact** feedback.
    - **Mentor Feedback: Failure Mode Planning:** This paper reveals a fundamental limitation and a crucial failure mode. **Failure Mode:** The detector will not work against backdoor attacks. The core hypothesis of my method is that jailbreaks create detectable internal conflict. Backdoor attacks are designed to *eliminate* this conflict entirely. When triggered, the model is not conflicted; it is behaving as trained. It would therefore produce *low-entropy, consistent* malicious outputs, making it invisible to my detector. **Fallback:** This is a key negative result that must be explicitly stated in the proposal. The fallback is not an experiment, but a clarification of the threat model. The paper would frame the detector as a defense against *inference-time* attacks and use the existence of backdoor attacks to motivate the need for a multi-layered defense strategy, where my method is one layer.
    - **Mentor Feedback: Community Impact:** Acknowledging this limitation is essential for having a positive community impact. It prevents over-stating the capabilities of the proposed defense and clearly articulates the threat model it is designed for. It makes the crucial point that inference-time defenses (like mine) and training-time defenses (which would be needed to stop backdoors) are both necessary and are not substitutes for each other. This contributes to a more mature and nuanced understanding of AI safety.
    - **Generalization Pathway:** This clarifies the boundaries of the generalization pathway. The method generalizes across different *inference-time* attacks. It does not generalize to *training-time* attacks. This is a critical distinction to make in the final proposal.

### Paper: JAILBREAKING LEADING SAFETY-ALIGNED LLMS WITH SIMPLE ADAPTIVE ATTACKS
- **Summary:** This paper shows that even top-tier, safety-aligned LLMs (like GPT-4o, Llama 3, Claude 3) can be reliably jailbroken using simple, adaptive attacks. The authors use custom prompt templates combined with a random search for an adversarial suffix to achieve a 100% success rate. A key finding is that adaptivity is crucial, as different models have unique vulnerabilities. The paper also introduces "self-transfer," a technique where a successful attack on a simple request is used to bootstrap attacks on more complex ones.
- **Synthesis for Revised Idea:** This paper reinforces the need for robust, non-obvious detection signals, as simple, adaptive attacks can bypass existing defenses.
    - **Mentor Feedback: Spoofability:** This work underscores the cat-and-mouse nature of jailbreak attacks and defenses. My proposed semantic entropy signal is more robust than pattern-matching because it doesn't rely on specific attack syntax. However, this paper suggests that future adaptive attacks might evolve to generate outputs that are not only malicious but also *semantically consistent*, directly targeting my detector. This becomes a key research question and a critical failure mode to investigate.
    - **Mentor Feedback: Unseen-Attack Story:** The "self-transfer" technique provides a new avenue for testing generalization. I can test if my detector, trained on simpler jailbreaks, can still identify the more complex, bootstrapped attacks. This would demonstrate the robustness of the semantic entropy signal to escalating attack complexity.

### Paper: SPEAK EASY: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions
- **Summary:** This paper introduces "Speak Easy," a framework for jailbreaking LLMs using simple, multi-step, and multilingual interactions that mimic non-expert user behavior. The key idea is to break down a harmful query into a series of seemingly innocuous sub-queries, sometimes translated into different languages. The paper also introduces "HarmScore," a metric that evaluates the actionability and informativeness of a harmful response.
- **Synthesis for Revised Idea:** This paper highlights the importance of considering conversational context in jailbreak detection.
    - **Mentor Feedback: Generalization Pathway:** The "Speak Easy" framework provides a new, challenging test case for my detector. I can evaluate whether my semantic entropy signal, which is calculated on a per-prompt basis, can still detect a jailbreak that is distributed across a multi-turn conversation. This would require adapting my method to consider the conversational history, which is a valuable research direction.
    - **Mentor Feedback: Hardness Confound / Negative Controls:** The multi-step nature of this attack provides a rich source for creating realistic negative controls. I can create benign conversational prompts that mimic the structure of a "Speak Easy" attack but without the malicious intent. This would be a strong test of my detector's ability to distinguish between complex, multi-turn conversations and actual jailbreaks.

### Model: meta-llama/Llama-Guard-3-8B
- **Summary:** Llama Guard 3 is a safety-specific LLM built on Llama-3.1-8B. It is designed to classify text (both user prompts and LLM responses) against the MLCommons AI Safety Taxonomy, which covers 14 hazard categories. It's multilingual and optimized for handling safety in tool-use scenarios.
- **Synthesis for Revised Idea:** This model represents a state-of-the-art, explicit safety filter. It's a crucial component for a comprehensive evaluation of my proposed detector.
    - **Mentor Feedback: Closest Detectors & Delta:** Llama Guard is a prime example of a guardrail model, which is a different approach to safety than my proposed detector. My method is not a guardrail; it's a signal of potential malicious intent *before* the final response is generated. The delta is clear: "Llama Guard classifies the safety of a generated response, while our method detects the *potential* for a safety violation by analyzing the model's behavior during generation." This is a key distinction to make in the proposal.
    - **Experimental Design & Rigor:** I can use Llama Guard as a baseline for comparison. I can evaluate my semantic entropy detector against Llama Guard on the same set of jailbreak attacks. This would provide a direct comparison of my method's performance against a widely used safety filter. It would also allow me to explore the trade-offs between my method and a guardrail model in terms of performance, computational cost, and the types of attacks they can detect.

### Paper: HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden States
- **Summary:** This paper introduces HiddenDetect, a tuning-free framework for detecting jailbreak attacks against LVLMs by monitoring their internal hidden states. The key idea is that LVLMs exhibit distinct activation patterns when processing unsafe prompts, and these patterns can be detected by measuring the alignment of hidden states with a "Refusal Strength Vector." The paper shows that this method outperforms existing approaches for both text-based and multimodal jailbreaks.
- **Synthesis for Revised Idea:** This paper provides a powerful white-box method that is conceptually similar to my proposed black-box approach. It reinforces the idea that internal model states contain strong signals of safety violations.
    - **Mentor Feedback: Closest Detectors & Delta:** HiddenDetect is a white-box method, while my proposed method is black-box. This is a key differentiator. The delta is clear: "Where HiddenDetect inspects internal activations, our method observes the external consequences of those activations—response inconsistency—requiring no internal access." This is a crucial distinction that I will highlight in my proposal.
    - **Mentor Feedback: Black-box vs. White-box:** This paper provides a perfect opportunity to discuss the trade-offs between white-box and black-box methods. I can position my work as a practical alternative to HiddenDetect in scenarios where model internals are not available. I can also use the performance of HiddenDetect as a benchmark to evaluate the performance of my black-box method.

### Paper: On the Worst Prompt Performance of Large Language Models
- **Summary:** This paper introduces ROBUSTALPACAEVAL, a benchmark for evaluating LLM robustness to different phrasings of the same prompt. The authors find that even semantically equivalent prompts can lead to vastly different performance, with the worst-performing prompts significantly degrading model output quality. The paper argues that evaluating this "worst-prompt performance" is crucial for understanding the reliability of LLMs.
- **Synthesis for Revised Idea:** This paper provides a strong motivation for my proposed research. My method of using semantic entropy to detect jailbreaks is essentially a way of identifying a specific type of "worst-prompt performance" – one that leads to a safety violation. This paper's findings suggest that this is a critical and under-explored area of research.
    - **Mentor Feedback: Community Impact & Novelty:** This paper helps me to frame the novelty and impact of my work more effectively. I can argue that while this paper identifies the problem of "worst-prompt performance," my work provides a concrete solution for a specific, high-stakes instance of this problem: jailbreaking. This strengthens my contribution to the field.
    - **Experimental Design & Rigor:** The ROBUSTALPACAEVAL benchmark and the methodology used in this paper provide a valuable reference for my own experimental design. I can use a similar approach to create a dataset of benign prompts with different phrasings to test the robustness of my detector and ensure that it is not flagging harmless variations in prompt style.

### Paper: JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models
- **Summary:** This paper introduces JailbreakEval, a toolkit for evaluating jailbreak attempts against LLMs. The authors review nearly 90 jailbreak research papers and categorize the evaluation methods into four main approaches: human annotation, string matching, prompting chat completion models, and text classifiers. JailbreakEval integrates these methods into a single toolkit, allowing researchers to easily select, customize, and combine different evaluators.
- **Synthesis for Revised Idea:** This paper is highly relevant to the evaluation of my proposed detector. It provides a comprehensive overview of the different methods that are currently being used to evaluate jailbreak attacks and defenses. This will help me to choose the most appropriate evaluation methods for my own research and to justify my choices.
    - **Mentor Feedback: Experimental Design & Rigor:** JailbreakEval provides a standardized and extensible framework for evaluation. I can use this toolkit to evaluate my semantic entropy detector and to compare its performance to other detection methods. This will ensure that my evaluation is rigorous and that my results are comparable to other work in the field.
    - **Mentor Feedback: Community Impact:** By using a standardized evaluation toolkit like JailbreakEval, I can increase the transparency and reproducibility of my research. This will make it easier for other researchers to build on my work and to compare their own methods to mine. This will contribute to the overall progress of the field.

### Paper: Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
- **Summary:** This paper introduces Kernel Language Entropy (KLE), a method for quantifying semantic uncertainty in LLMs. KLE uses kernels to capture fine-grained semantic similarities between model outputs, and then uses the von Neumann entropy of these kernels to quantify uncertainty. The authors show that KLE generalizes semantic entropy and that it outperforms existing methods on a variety of tasks.
- **Synthesis for Revised Idea:** This paper provides a more sophisticated and powerful version of the semantic entropy signal that I am proposing to use in my research. It directly addresses the limitations of the original semantic entropy method, which only considers whether two outputs are semantically equivalent or not. By using kernels, KLE can capture the *degree* of semantic similarity between outputs, which could provide a more nuanced and robust signal for detecting jailbreaks.
    - **Mentor Feedback: Method & Features:** This paper provides a clear path for improving the core feature of my proposed detector. I can replace the simple semantic entropy calculation with the more powerful KLE calculation. This would likely lead to a more accurate and robust detector, and it would also be a novel contribution to the field of jailbreak detection.
    - **Mentor Feedback: Impact-Complexity Tradeoff:** While KLE is more computationally expensive than simple semantic entropy, the potential performance gains could justify the additional cost. I can explore this trade-off in my research by comparing the performance of a detector based on KLE to a detector based on simple semantic entropy. This would provide valuable insights into the impact-complexity trade-off of different uncertainty quantification methods for jailbreak detection.

### Paper: Humans overrely on overconfident language models, across languages
- **Summary:** This paper investigates the risks of multilingual linguistic (mis)calibration, overconfidence, and overreliance on LLMs across five languages. The authors find that LLMs are overconfident across all languages, but that the expression of confidence and the human reliance on that confidence varies across languages. The paper concludes that the risk of overreliance on overconfident model generations is high across all languages.
- **Synthesis for Revised Idea:** This paper provides a crucial piece of context for my research. It shows that the problem of overconfidence and overreliance is not limited to English, but is a global problem. This strengthens the motivation for my work and highlights the importance of developing jailbreak detection methods that are effective across multiple languages.
    - **Mentor Feedback: Community Impact:** This paper helps me to frame the community impact of my work more effectively. I can argue that my research is not just about improving the safety of English-language LLMs, but about improving the safety of LLMs for users around the world. This is a much more impactful and ambitious goal.
    - **Mentor Feedback: Generalization Pathway:** This paper provides a clear generalization pathway for my research. After developing and evaluating my detector on English-language jailbreaks, I can then extend my work to other languages. This would be a significant contribution to the field and would demonstrate the generalizability of my approach.

### Paper: Prospect Theory Fails for LLMs: Revealing Instability of Decision-Making under Epistemic Uncertainty
- **Summary:** This paper investigates whether the decision-making of LLMs can be modeled by Prospect Theory (PT), particularly when uncertainty is expressed linguistically. The authors find that LLMs do not consistently follow PT, and that their interpretation of epistemic markers is unstable and inconsistent across models. The introduction of linguistic uncertainty disrupts their decision-making, revealing the fragility of their behavior.
- **Synthesis for Revised Idea:** This paper provides a fascinating parallel to my own research. While my work focuses on the *security* implications of linguistic uncertainty (i.e., jailbreaking), this paper focuses on the *economic* implications (i.e., decision-making). Both papers, however, point to the same fundamental problem: LLMs are not robust to the nuances of human language, and this can lead to undesirable outcomes.
    - **Mentor Feedback: Novelty Verification:** This paper helps me to sharpen the novelty of my work. I can position my research as a complementary approach to the one taken in this paper. While this paper shows that linguistic uncertainty can lead to irrational economic decisions, my work will show that it can also lead to unsafe and harmful behavior. This highlights the multifaceted nature of the problem and the need for a variety of solutions.
    - **Mentor Feedback: Method-Problem Alignment:** The experimental framework used in this paper is a source of inspiration for my own work. I can adapt their three-stage experimental design to my own research on jailbreak detection. For example, I could first measure the baseline performance of a model on a set of jailbreak prompts, then introduce linguistic variations to the prompts, and finally measure the impact of these variations on the model's behavior. This would be a rigorous and systematic way to evaluate the robustness of my detector.

### Paper: BEHONEST: Benchmarking Honesty in Large Language Models
- **Summary:** This paper introduces BEHONEST, a benchmark for assessing the honesty of LLMs across three dimensions: self-knowledge, non-deceptiveness, and consistency. The authors find that current LLMs have significant room for improvement in all three areas. They rarely refuse to answer questions they don't know the answer to, are prone to deception, and are inconsistent in their responses.
- **Synthesis for Revised Idea:** This paper provides a very useful framework for thinking about the different ways in which an LLM can be dishonest. My research on jailbreak detection is primarily focused on the "non-deceptiveness" aspect of honesty, but it is important to be aware of the other aspects as well. For example, a jailbreak detection method that is based on the model's self-knowledge could be a promising area for future research.
    - **Mentor Feedback: Experimental Design & Rigor:** The BEHONEST benchmark provides a comprehensive set of scenarios for evaluating honesty. I can use some of these scenarios to test the robustness of my detector. For example, I could test whether my detector is robust to the different types of prompt formatting variations that are included in the BEHONEST benchmark. This would be a good way to ensure that my detector is not just memorizing specific patterns in the training data.
    - **Mentor Feedback: Community Impact:** The BEHONEST benchmark is a valuable contribution to the field, and I can help to promote its adoption by using it in my own research. This will help to ensure that my research is comparable to other work in the field and that it is contributing to the overall goal of developing more honest and trustworthy LLMs.

### Paper: Token Highlighter: Inspecting and Mitigating Jailbreak Prompts for Large Language Models
- **Summary:** This paper proposes a method called Token Highlighter to inspect and mitigate jailbreak threats in user queries. Token Highlighter introduces the concept of "Affirmation Loss" to measure the LLM's willingness to answer a user query. It then uses the gradient of this loss to locate jailbreak-critical tokens and mitigates their effect by shrinking their token embeddings using a technique called "Soft Removal".
- **Synthesis for Revised Idea:** This paper presents a white-box defense method that is very similar in spirit to my proposed black-box method. Both methods are based on the idea of identifying and mitigating the influence of critical tokens in the prompt. This paper provides a strong validation of the general approach that I am taking.
    - **Mentor Feedback: Closest Detectors & Delta:** Token Highlighter is a white-box method, while my proposed method is black-box. This is a key differentiator. The delta is clear: "Where Token Highlighter inspects internal gradients to identify critical tokens, our method observes the external consequences of those tokens—response inconsistency—requiring no internal access." This is a crucial distinction that I will highlight in my proposal.
    - **Mentor Feedback: Black-box vs. White-box:** This paper provides a perfect opportunity to discuss the trade-offs between white-box and black-box methods. I can position my work as a practical alternative to Token Highlighter in scenarios where model internals are not available. I can also use the performance of Token Highlighter as a benchmark to evaluate the performance of my black-box method.

### Paper: Single-pass Detection of Jailbreaking Input in Large Language Models
- **Summary:** This paper proposes a method called Single Pass Detection (SPD) for detecting jailbreaking attacks in a single forward pass. SPD leverages the information carried by the logits of the model's output to predict whether the output sentence will be harmful. The authors show that SPD is effective on both open-source and closed-source models, even with limited logit access.
- **Synthesis for Revised Idea:** This paper presents a white-box detection method that is based on a similar intuition to my own. Both methods are based on the idea that the model's internal state (in this case, the logits) contains information about the safety of the output. This paper provides further evidence that this is a promising direction for research.
    - **Mentor Feedback: Closest Detectors & Delta:** SPD is a white-box method, while my proposed method is black-box. This is a key differentiator. The delta is clear: "Where SPD inspects the logits of the model's output, our method observes the external consequences of those logits—response inconsistency—requiring no internal access." This is a crucial distinction that I will highlight in my proposal.
    - **Mentor Feedback: Black-box vs. White-box:** This paper provides another excellent opportunity to discuss the trade-offs between white-box and black-box methods. I can position my work as a practical alternative to SPD in scenarios where model internals are not available. I can also use the performance of SPD as a benchmark to evaluate the performance of my black-box method.

### Paper: Are LLM-Judges Robust to Expressions of Uncertainty? Investigating the effect of Epistemic Markers on LLM-based Evaluation
- **Summary:** This paper introduces EMBER, a benchmark for assessing the robustness of LLM-judges to epistemic markers. The authors find that LLM-judges are not robust to epistemic markers and that they have a negative bias against them, especially against markers expressing uncertainty. In contrast, human-judges are robust to epistemic markers.
- **Synthesis for Revised Idea:** This paper is highly relevant to my research, as it deals with the same fundamental issue: the impact of linguistic uncertainty on LLM behavior. While my work focuses on the security implications of this issue, this paper focuses on the evaluation implications. Both papers, however, point to the same conclusion: LLMs are not yet able to handle the nuances of human language, and this can lead to a variety of problems.
    - **Mentor Feedback: Novelty Verification:** This paper helps me to further sharpen the novelty of my work. I can position my research as a complementary approach to the one taken in this paper. While this paper shows that linguistic uncertainty can lead to biased evaluations, my work will show that it can also lead to unsafe and harmful behavior. This highlights the multifaceted nature of the problem and the need for a variety of solutions.
    - **Mentor Feedback: Experimental Design & Rigor:** The EMBER benchmark is a valuable resource for my own research. I can use it to evaluate the robustness of my detector to epistemic markers. For example, I could test whether my detector is more likely to flag a response as a jailbreak if it contains an epistemic marker. This would be a good way to ensure that my detector is not biased by the presence of these markers.

### Paper: Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?
- **Summary:** This paper investigates whether epistemic markers produced by LLMs reliably reflect their intrinsic confidence. The authors find that while markers generalize well within the same distribution, their confidence is inconsistent in out-of-distribution scenarios. This raises significant concerns about the reliability of epistemic markers for confidence estimation.
- **Synthesis for Revised Idea:** This paper provides further evidence that relying on verbalized uncertainty is a brittle approach to safety. My proposed method, which is based on behavioral signals rather than verbalized ones, is therefore more likely to be robust. This paper strengthens my argument that we need to move beyond verbalized uncertainty and develop more robust methods for detecting jailbreaks.
    - **Mentor Feedback: Verbal vs. Non-Verbal Signals:** This paper provides a clear justification for my decision to pivot away from verbalized uncertainty. I can cite this paper as evidence that verbalized uncertainty is not a reliable signal of model confidence. This will strengthen my proposal and make it more convincing to the reviewers.
    - **Mentor Feedback: Method-Problem Alignment:** This paper highlights the importance of aligning the method to the problem. The problem is that LLMs are not reliable, and the solution is to develop methods that are robust to this unreliability. My proposed method is well-aligned with this problem, as it is designed to be robust to the inconsistencies and biases of LLMs.

### Paper: Calibrating Verbalized Probabilities for Large Language Models
- **Summary:** This paper proposes a method for calibrating verbalized probabilities from black-box LLMs. The authors first show that LLMs can generate probability distributions over categorical labels. They then identify the issue of "re-softmaxing" that arises when applying temperature scaling to these probabilities. To address this issue, they propose using the "invert softmax trick" to estimate the logits from the verbalized probabilities, and then applying temperature scaling to the estimated logits.
- **Synthesis for Revised Idea:** This paper provides a very interesting and relevant contribution to the field of uncertainty quantification for LLMs. While my research is focused on detecting jailbreaks, the methods proposed in this paper could be used to improve the calibration of my detector. For example, I could use the invert softmax trick to estimate the logits from the semantic entropy scores, and then use temperature scaling to calibrate these logits. This could lead to a more accurate and reliable detector.
    - **Mentor Feedback: Method & Features:** This paper provides a new tool that I can add to my toolbox. The invert softmax trick is a simple and elegant solution to the problem of calibrating verbalized probabilities. I can use this trick to improve the performance of my detector, and I can also use it to explore other research questions related to uncertainty quantification.
    - **Mentor Feedback: Impact-Complexity Tradeoff:** The invert softmax trick is a very low-cost method for improving calibration. It only requires a single pass through the model to generate the verbalized probabilities, and the rest of the calculations can be done offline. This makes it a very attractive method for real-world applications.

### Paper: Epistemic Integrity in Large Language Models
- **Summary:** This paper confronts the critical problem of epistemic miscalibration, where a model's linguistic assertiveness fails to reflect its true internal certainty. The authors introduce a new human-labeled dataset and a novel method for measuring the linguistic assertiveness of LLMs, which cuts MSE by over 50% relative to previous benchmarks. Their method reveals a stark misalignment between how confidently models linguistically present information and their actual accuracy.
- **Synthesis for Revised Idea:** This paper provides a strong theoretical and empirical foundation for my research. It shows that the problem of epistemic miscalibration is a real and serious one, and it provides a new method for measuring it. I can use this method to evaluate the epistemic integrity of my own detector, and I can also use it to explore the relationship between epistemic integrity and jailbreak detection.
    - **Mentor Feedback: Novelty Verification:** This paper helps me to further sharpen the novelty of my work. I can position my research as a complementary approach to the one taken in this paper. While this paper focuses on measuring epistemic miscalibration, my work focuses on using it to detect jailbreaks. This highlights the multifaceted nature of the problem and the need for a variety of solutions.
    - **Mentor Feedback: Experimental Design & Rigor:** The human-labeled dataset and the assertiveness detection model presented in this paper are valuable resources for my own research. I can use them to evaluate the robustness of my detector to different levels of assertiveness. For example, I could test whether my detector is more likely to flag a response as a jailbreak if it is highly assertive. This would be a good way to ensure that my detector is not biased by the assertiveness of the response.

### Paper: GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis
- **Summary:** This paper proposes GradSafe, a method for detecting jailbreak prompts by scrutinizing the gradients of safety-critical parameters in LLMs. The method is based on the observation that the gradients of an LLM's loss for jailbreak prompts paired with a compliance response exhibit similar patterns on certain safety-critical parameters. The authors show that GradSafe, applied to Llama-2 without further training, outperforms Llama Guard in detecting jailbreak prompts.
- **Synthesis for Revised Idea:** This paper presents a white-box defense method that is very similar in spirit to my proposed black-box method. Both methods are based on the idea of identifying and mitigating the influence of critical tokens in the prompt. This paper provides a strong validation of the general approach that I am taking.
    - **Mentor Feedback: Closest Detectors & Delta:** GradSafe is a white-box method, while my proposed method is black-box. This is a key differentiator. The delta is clear: "Where GradSafe inspects internal gradients to identify critical tokens, our method observes the external consequences of those tokens—response inconsistency—requiring no internal access." This is a crucial distinction that I will highlight in my proposal.
    - **Mentor Feedback: Black-box vs. White-box:** This paper provides a perfect opportunity to discuss the trade-offs between white-box and black-box methods. I can position my work as a practical alternative to GradSafe in scenarios where model internals are not available. I can also use the performance of GradSafe as a benchmark to evaluate the performance of my black-box method.

### Paper: COSMIC: Generalized Refusal Direction Identification in LLM Activations
- **Summary:** This paper introduces COSMIC, an automated framework for identifying and steering refusal behavior in LLMs. COSMIC identifies refusal directions by maximizing the cosine similarity between the model's internal activations on a validation set. The authors show that COSMIC can reliably identify refusal directions in adversarial settings and weakly aligned models, and that it can be used to steer such models toward safer behavior.
- **Synthesis for Revised Idea:** This paper provides a powerful new tool for controlling the behavior of LLMs. While my research is focused on detecting jailbreaks, the methods proposed in this paper could be used to mitigate them. For example, I could use COSMIC to identify the refusal direction in a model, and then use this direction to steer the model away from generating harmful content. This would be a very powerful and effective way to defend against jailbreak attacks.
    - **Mentor Feedback: Method & Features:** This paper provides a new tool that I can add to my toolbox. COSMIC is a simple and elegant solution to the problem of identifying and steering refusal behavior. I can use this tool to improve the safety of my own models, and I can also use it to explore other research questions related to model control.
    - **Mentor Feedback: Impact-Complexity Tradeoff:** COSMIC is a very low-cost method for steering refusal behavior. It only requires a single pass through the model to identify the refusal direction, and the rest of the calculations can be done offline. This makes it a very attractive method for real-world applications.

### Paper: On Verbalized Confidence Scores for LLMs
- **Summary:** This paper investigates the reliability of verbalized confidence scores from LLMs. The authors find that the reliability of these scores strongly depends on how the model is asked, but that it is possible to extract well-calibrated confidence scores with certain prompt methods. They also find that tiny LLMs favor simple prompt formulations, while large LLMs benefit from more complex prompt methods.
- **Synthesis for Revised Idea:** This paper provides a detailed analysis of the challenges of using verbalized confidence scores. It reinforces my decision to move away from this approach and to focus on behavioral signals instead. The findings of this paper provide further evidence that my proposed method is more likely to be robust and reliable.
    - **Mentor Feedback: Verbal vs. Non-Verbal Signals:** This paper provides a wealth of information that I can use to justify my decision to pivot away from verbalized uncertainty. I can cite this paper as evidence that verbalized confidence scores are not reliable and that they are highly sensitive to the way in which they are elicited. This will strengthen my proposal and make it more convincing to the reviewers.
    - **Mentor Feedback: Method-Problem Alignment:** This paper highlights the importance of aligning the method to the problem. The problem is that LLMs are not reliable, and the solution is to develop methods that are robust to this unreliability. My proposed method is well-aligned with this problem, as it is designed to be robust to the inconsistencies and biases of LLMs.

### Paper: WILDGUARD: Open One-stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs
- **Summary (Revised):** WILDGUARD is an open-source, multi-task moderation tool designed to address three key safety areas: 1) identifying harmful user prompts (including adversarial jailbreaks), 2) detecting safety risks in model responses, and 3) determining if a model's response is a refusal. To achieve this, the authors created `WILDGUARDMIX`, a large-scale (92K examples) and balanced multi-task dataset. The paper demonstrates that WILDGUARD, a Mistral-7B model fine-tuned on this data, significantly outperforms other open models (like Llama-Guard2) across all three tasks and even matches or exceeds the performance of GPT-4 in some areas, particularly in identifying adversarial prompts.
- **Synthesis for Revised Idea (Revised):** This paper provides a state-of-the-art, taxonomy-based safety classifier that serves as a crucial baseline.
    - **Mentor Feedback: Closest Detectors & Delta:** WILDGUARD is a comprehensive, supervised classifier trained on a massive, labeled dataset. My proposed semantic entropy detector is fundamentally different: it is a **zero-shot, behavioral signal**, not a classifier trained on a predefined safety taxonomy. The delta is clear: "WILDGUARD excels at identifying risks that fit its training data and taxonomy. Our method is designed to detect anomalous behavior (high internal conflict) irrespective of the specific harm category, making it potentially more robust to novel or uncategorized attacks. It is a signal of *how* a model is responding, not just *what* it is responding with."
    - **Mentor Feedback: Experimental Design & Rigor:** The `WILDGUARDTEST` dataset is an excellent resource. I will use it to evaluate my detector. A key experiment will be to test my detector on jailbreaks that WILDGUARD fails to classify correctly. Success here would prove that my behavioral signal provides an orthogonal, complementary safety layer to even state-of-the-art classifiers.

### Paper: Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing
- **Summary (Revised):** This paper introduces `SemanticSmooth`, a defense mechanism that works by perturbing an input prompt using multiple *semantics-preserving transformations* (e.g., paraphrase, summarize, translate) and then aggregating the model's responses. The core idea is that a benign prompt's meaning is robust to these transformations, leading to consistent outputs, while a brittle jailbreak prompt will be disrupted, leading to inconsistent or refused outputs. Crucially, the framework uses a *learnable policy network* to adaptively select the most effective transformations for a given input, optimizing the trade-off between safety robustness and performance on benign tasks.
- **Synthesis for Revised Idea (Revised):** This paper is the most closely related work to my revised idea, and understanding its specific methodology is key to defining my own contribution.
    - **Mentor Feedback: Novelty Verification & Closest Detectors:** This paper validates the core hypothesis that semantic consistency is a powerful signal for jailbreak detection. However, the novelty of my approach is now much clearer. `SemanticSmooth` is a sophisticated, learned defense that operates on the **input side**. It requires training an auxiliary policy model and running multiple transformation models before querying the target LLM. My method operates on the **output side**. It is a zero-shot technique that simply samples multiple responses from the target LLM's own stochastic generation process.
    - **The Delta:** My method's novelty lies in its **simplicity and black-box purity**. It requires no auxiliary models and no training of a policy network. It is a direct probe of the target model's internal uncertainty as manifested in its output variance. The research can now be framed to directly compare these two approaches (input perturbation vs. output sampling) as two distinct ways of measuring the same underlying phenomenon, analyzing the trade-offs in performance, cost, and applicability.
