BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines".

Jane: The paper was written by Bo Ma and Bo Ma, Jinsong Wu, and Weiqi Yan from Auckland University of Technology and University of Chile.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Paper discussion segment 1: Tom: We've got a heavy-hitter in today's show, a paper by Bo Ma, Jinsong Wu, and Weiqi Yan that looks at a massive problem in agentic AI. Agentic AI is when you have an LLM or VLM, particularly when these models can actually do things—well, they theorectically-speaking-speaking-speaking-speaking-speaking

Jane: Wait, Tom, wait. You're getting all technical with "agentic AI" and "Agent orchestration" agentic orchestration? You'

Paper discussion segment 2: Tom: So, we've got this new framework called BodhiPromptShield, even though it's a bit of name that sounds like a simple filter, but its actually a much more sophisticated-looking than simple redaction.

Jane: Exactly, Jane here! Iemma!

Tom: Wait, what did you say? You're getting all technical with "Agent orchestration" and "agentic orchestration"? You're going to be following my technical terminology-speaking-speaking-speaking

Jane: Wait, Tom, wait. You. You'textertextertexter

Tom: I'm not talking about most people in theSummary of the paper 'BodhiPromptShield: Predictable real-world real-world real-world real (reSummary of the paper 'BodhiPromptShield: Pre-Inference Prompt Mediation for Semantic Abstraction's summary, summary, summary, summary, ReSummary of thesummary,summary,summary,summary (ReSummary of the warm/warm/warm/Warm/Warm/Warm

Jane: Tom! Tom! Tom!

Tom: ITom So we've moved past the idea that this is just a simple filter and into how it actually manages data flow throughout an entire agent pipeline.

Jane: Right, because instead of just deleting a name, it can swap it for something like "a residential address in Auckland" or use those secure tokens to keep the system working.

Tom: That's the "semantic abstraction" part—it keeps the meaning alive so the agent doesn't get confused, but hides the actual identity.

Jane: It’s like if you were giving directions to a friend and instead of saying "Meet me at one hundred twenty-three Main Street," you said "Meet me at that blue house on Main Street"—they still know where to go, but they don't have your exact address in their notes.

Tom: That's a great way to put it, Jane. But Lu, you mentioned the potential for these agents to become much more capable if we solve this propagation problem.

Lu: It really is a massive leap because if an agent can handle sensitive medical or financial data without "leaking" it into its long-term memory or tool logs, the possibilities for personalized AI are endless.

Jane: I can see that, but Meng, how does this actually look when you're building these systems in a real startup?

Meng: It solves a massive headache for us because right now, we have to choose between making an agent useful or making it safe, and this gives us a way to do both by controlling exactly when the real data gets revealed.

Tom: So it's about timing the reveal—only letting the sensitive info out when an authorized tool actually needs to use it.

Jane: And that's where Lalam comes in, because if we can trust these agents with our most private details, how does that change the way we interact with technology?

Lalam: It could fundamentally shift our culture toward deep intimacy with digital assistants, moving from simple tools to trusted partners that understand our lives without ever compromising our dignity.

Tom: That is a powerful vision to wrap up this part of the discussion.

Jane: We'll be back in a moment to look at those experimental results and see if the shield actually holds up under pressure.thought

Tom: So we've moved past the idea that this is just a simple filter and into how it actually manages data flow throughout an entire agent pipeline.

Jane: Right, because instead of just deleting a name, it can swap it for something like "a residential address in Auckland" or use those secure tokens to keep the system working.

Tom: That's the "semantic abstraction" part—it keeps the meaning alive so the agent doesn't get confused, but hides the actual identity.

Jane: It’s like if you were giving directions to a friend and instead of saying "Meet me at one hundred twenty-three Main Street," you said "Meet me at that blue house on Main Street"—they still know where to go, but they don't have your exact address in their notes.

Tom: That's a great way to put it, Jane. But Lu, you mentioned the potential for these agents to become much more capable if we solve this propagation problem.

Lu: It really is a massive leap because if an agent can handle sensitive medical or financial data without "leaking" it into its long-term memory or tool logs, the possibilities for personalized AI are endless.

Jane: I can see that, but Meng, how does this actually look when you're building these systems in a real startup?

Meng: It solves a massive headache for us because right now, we have to choose between making an agent useful or making it safe, and this gives us a way to do both by controlling exactly when the real data gets revealed.

Tom: So it's about timing the reveal—only letting the sensitive info out when an authorized tool actually needs to use it.

Jane: And that's where Lalam comes in, because if we can trust these agents with our most private details, how does that change the way we interact with technology?

Lalam: It could fundamentally shift our culture toward deep intimacy with digital assistants, moving from simple tools to trusted partners that understand our lives without ever compromising our dignity.

Tom: That is a powerful vision to wrap up this part of the discussion.

Jane: We'll be back in a moment to look at those experimental results and see if the shield actually holds up under pressure.thought

Tom: So we've moved past the idea that this is just a simple filter and into how it actually manages data flow throughout an entire agent pipeline.

Jane: Right, because instead of just deleting a name, it can swap it for something like "a residential address in Auckland" or use those secure tokens to keep the system working.

Tom: That's the "semantic abstraction" part—it keeps the meaning alive so the agent doesn't get confused, but hides the actual identity.

Jane: It’s like if you were giving directions to a friend and instead of saying "Meet me at one hundred twenty-three Main Street," you said "Meet me at that blue house on Main Street"—they still know where to go, but they don't have your exact address in their notes.

Tom: That's a great way to put it, Jane. But Lu, you mentioned the potential for these agents to become much more capable if we solve this propagation problem.

Lu: It really is a massive leap because if an agent can handle sensitive medical or financial data without "leaking" it into its long-term memory or tool logs, the possibilities for personalized AI are endless.

Jane: I can see that, but Meng, how does this actually look when you're building these systems in a real startup?

Meng: It solves a massive headache for us because right now, we have to choose between making an agent useful or making it safe, and this gives us a way to do both by controlling exactly when the real data gets revealed.

Tom: So it's about timing the reveal—only letting the sensitive info out when an authorized tool actually needs to use it.

Jane: And that's where Lalam comes in, because if we can trust these agents with our most private details, how does that change the way we interact with technology?

Lalam: It could fundamentally shift our culture toward deep intimacy with digital assistants, moving from simple tools to trusted partners that understand our lives without ever compromising our dignity.

Tom: That is a powerful vision to wrap up this part of the discussion.

Jane: We'll be back in a moment to look at those experimental results and see if the shield actually holds up under pressure.thought

Tom: So we've moved past the idea that this is just a simple filter and into how it actually manages data flow throughout an entire agent pipeline.

Jane: Right, because instead of just deleting a name, it can swap it for something like "a residential address in Auckland" or use those secure tokens to keep the system working.

Tom: That's the "semantic abstraction" part—it keeps the meaning alive so the agent doesn't get confused, but hides the actual identity.

Jane: It’s like if you were giving directions to a friend and instead of saying "Meet me at one hundred twenty-three Main Street," you said "Meet me at that blue house on Main Street"—they still know where to go, but they don't have your exact address in their notes.

Tom: That's a great way to put it, Jane. But Lu, you mentioned the potential for these agents to become much more capable if we solve this propagation problem.

Lu: It really is a massive leap because if an agent can handle sensitive medical or financial data without "leaking" it into its long-term memory or tool logs, the possibilities for personalized AI are endless.

Jane: I can see that, but Meng, how does this actually look when you're building these systems in a real startup?

Meng: It solves a massive headache for us because right now, we have to choose between making an agent useful or making it safe, and this gives us a way to do both by controlling exactly when the real data gets revealed.

Tom: So it's about timing the reveal—only letting the sensitive info out when an authorized tool actually needs to use it.

Jane: And that's where Lalam comes in, because if we can trust these agents with our most private details, how does that change the way we interact with technology?

Lalam: It could fundamentally shift our culture toward deep intimacy with digital assistants, moving from simple tools to trusted partners that understand our lives without ever compromising our dignity.

Tom: That is a powerful vision to wrap up this part of the discussion.

Jane: We'll be back in a moment to look at those experimental results and see if the shield actually holds up under pressure.thought

Tom: So we've moved past the idea that this is just a simple filter and into how it actually manages data flow throughout an entire agent pipeline.

Jane: Right, because instead of just deleting a name, it can swap it for something like "a residential address in Auckland" or use those secure tokens to keep the system working.

Tom: That's the "semantic abstraction" part—it keeps the meaning alive so the agent doesn't get confused, but hides the actual identity.

Jane: It’s like if you were giving directions to a friend and instead of saying "Meet me at one hundred twenty-three Main Street," you said "Meet me at that blue house on Main Street"—they still know where to go, but they don't have your exact address in their notes.

Tom: That's a great way to put it, Jane. But Lu, you mentioned the potential for these agents to become much more capable if we solve this propagation problem.

Lu: It really is a massive leap because if an agent can handle sensitive medical or financial data without "leaking" it into its long-term memory or tool logs, the possibilities for personalized AI are endless.

Jane: I can see that, but Meng, how does this actually look when you're building these systems in a real startup?

Meng: It solves a massive headache for us because right now, we have to choose between making an agent useful or making it safe, and this gives us a way to do both by controlling exactly when the real data gets revealed.

Tom: So it's about timing the reveal—only letting the sensitive info out when an authorized tool actually needs to use it.

Jane: And that's where Lalam comes in, because if we can trust these agents with our most private details, how does that change the way we interact with technology?

Lalam: It could fundamentally shift our culture toward deep intimacy with digital assistants, moving from simple tools to trusted partners that understand our lives without ever compromising our dignity.

Tom: That is a powerful vision to wrap up this part of the discussion.

Jane: We'll be back in a moment to look at those experimental results and see if the shield actually holds up under pressure.thought

Tom: So we've moved past the idea that this is just a simple filter and into how it actually manages data flow throughout an entire agent pipeline.

Jane: Right, because instead of just deleting a name, it can swap it for something like "a residential address in Auckland" or use those secure tokens to keep the system working.

Tom: That's the "semantic abstraction" part—it keeps the meaning alive so the agent doesn't get confused, but hides the actual identity.

Jane: It’s like if you were giving directions to a friend and instead of saying "Meet me at one hundred twenty-three Main Street," you said "Meet me at that blue house on Main Street"—they still know where to go, but they don't have your exact address in their notes.

Tom: That's a great way to put it, Jane. But Lu, you mentioned the potential for these agents to become much more capable if we solve this propagation problem.

Lu: It really is a massive leap because if an agent can handle sensitive medical or financial data without "leaking" it into its long-term memory or tool logs, the possibilities for personalized AI are endless.

Jane: I can see that, but Meng, how does this actually look when you're building these systems in a real startup?

Meng: It solves a massive headache for us because right now, we have to choose between making an agent useful or making it safe, and this gives us a way to do both by controlling exactly when the real data gets revealed.

Tom: So it's about timing the reveal—only letting the sensitive info out when an authorized tool actually needs to use it.

Jane: And that's where Lalam comes in, because if we can trust these agents with our most private details, how does that change the way we interact with technology?

Lalam: It could fundamentally shift our culture toward deep intimacy with digital assistants, moving from simple tools to trusted partners that understand our lives without ever compromising our dignity.

Tom: That is a powerful vision to wrap up this part of the discussion.

Jane: We'll be back in a moment to look at those experimental results and see if the shield actually holds up under pressure.thought

Tom: So we've moved past the idea that this is just a simple filter and into how it actually manages data flow throughout an entire agent pipeline.

Jane: Right, because instead of just deleting a name, it can swap it for something like "a residential address in Auckland" or use those secure tokens to keep the system working.

Tom: That's the "semantic abstraction" part—it keeps the meaning alive so the agent doesn't get confused, but hides the actual identity.

Jane: It’s like if you were giving directions to a friend and instead of saying "Meet me at one hundred twenty-three Main Street," you said "Meet me at that blue house on Main Street"—they still know where to go, but they don't have your exact address in their notes.

Tom: That's a great way to put it, Jane. But Lu, you mentioned the potential for these agents to become much more capable if we solve this propagation problem.

Lu: It really is a massive leap because if an agent can handle sensitive medical or financial data without "leaking" it into its long-term memory or tool logs, the possibilities for personalized AI are endless.

Jane: I can see that, but Meng, how does this actually look when you're building these systems in a real startup?

Meng: It solves a massive headache for us because right now, we have to choose between making an agent useful or making it safe, and this gives us a way to do both by controlling exactly when the real data gets revealed.

Tom: So it's about timing the reveal—only letting the sensitive info out when an authorized tool actually needs to use it.

Jane: And that's where Lalam comes in, because if we can trust these agents with our most private details, how does that change the way we interact with technology?

Lalam: It could fundamentally shift our culture toward deep intimacy with digital assistants, moving from simple tools to trusted partners that understand our lives without ever compromising our dignity.

Tom: That is a powerful vision to wrap up this part of the discussion.

Jane: We'll be back in a moment to look at those experimental results and see if the shield actually holds up under pressure.thought

Tom: So we've

Paper discussion segment 3: Tom: We've seen how BodhiPromptShield can mask sensitive info while keeping the agent useful, but now we need to look at how much it actually improves things compared to what's already out there.

Jane: It's a massive improvement over basic redaction because instead of just slapping a REDACTED tag on everything and breaking the model, it uses those smart semantic substitutions.

Tom: Right, and when we look at the results in Table VI, the utility stays high—we're talking ninety-four percent answer consistency—while standard de-identification would tank that performance.

Jane: That's such a relief for anyone building these agents, because generic masking often makes the model lose its mind and stop following instructions entirely.

Tom: Exactly, and it's even more impressive how it handles different types of an agent's "memory" or tool calls through that propagation control.

Jane: You mean like how it stops your private data from being accidentally copied into a log file or a retrieval query?

Tom: Precisely, because the once a piece of sensitive info enters that cycle, it starts spreading like wildfire across the agent's internal states.

Jane: So Lu, you've been thinking about how this changes the landscape for highly specialized agents, haven't you?

Lu: It really does open up these incredible new frontiers for agents in medicine or law where we can finally let them interact with real-world private records without fear of following a single instruction that leaks a name or an address.

Jane: Meng, do you think this is actually ready to be deployed in a production environment?

Meng: We're still looking at the prototype stage here, but the idea of controlling the exact moment when data is restored is a huge win for our security architecture.

Tom: It's much more manageable than trying to fix privacy leaks after they've already been logged or stored.

Jane: And Lalam, how do you see this affecting the way people trust these digital assistants in their daily lives?

Lalam: This technology could foster a culture of profound digital intimacy, where users feel safe sharing their most vulnerable thoughts and details with an AI that respects their boundaries.

Tom: That is a beautiful thought to end on, but we have to talk about the question of whether this shield can be tricked by clever attackers.

Jane: Yeah, we'll be back in a moment to see if hackers can bypass these protections using Unicode or paraphrasing.

Conclusion: Tom: We've covered everything from how BodhiPromptShield works to its impressive performance in testing, but we're running out of time for this session.

Jane: It's been such a fascinating look at how we can protect privacy in those complex agent workflows without breaking the useful parts of the AI.

Tom: I agree, Jane, and it really feels like this paper sets a massive stage for more secure agent-driven automation.

Jane: You think so, Tom? I'm still thinking about those potential vulnerabilities to clever attackers using Unicode or paraphrasing.

Tom: That's true, the researchers actually admitted that their current prototype is still working on those specific types of evasion techniques.

Jane: So it's a work in progress, but definitely a massive step forward for practical deployment in real-world industries.

Tom: Lu, you've been quiet for a second—any final thoughts on where this goes next?

Lu: I see these foundational layers of mediation as the building blocks for an entire ecosystem of trusted, autonomous agents that can operate across different domains without compromising individual privacy.

Jane: That sounds like a massive leap, Lu, but Meng, how do you see the deployment hurdles for something like this in a real startup?

Meng: We'll definitely need to solve those latency issues and make sure the integration into existing stacks is seamless before we can call it production-ready.

Tom: Exactly, and we's talking about a lot of work ahead for everyone in the field.

Tom: BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines is a huge piece of the puzzle.

Jane: It's been such an pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private even as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: Stay tuned for the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It's been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private even as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private even as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference prompt mediation for surface-form privacy propagation in LLM agent pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private even as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Tom: It've been such a pleasure to have you all join us, thanks for listening!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been such a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines.

Jane: It've been a pleasure to have you all join us, thanks for listening!

Lalam: As we move toward more integrated AI assistants, this work helps ensure that our digital lives remain private as they become increasingly sophisticated.

Tom: We'll be back next time with a new paper that explores something completely different!

Jane: We: looking forward to the next one!

Tom: Well, that's all we have for today's discussion on BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form