---
title: "Dissertation"
---

## MORAL COVER AND WATCHFUL EYES: THE LIMITS OF IN-CONTEXT INTERVENTIONS ON LLM DECEPTION

Jayrup Nakawala \
BSc Data Science and Artificial Intelligence \
University of East London — May 2026 \
Supervisor: Dr. Aloysius Adotey Edoh

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## ABSTRACT

As LLMs are entrusted with real-world tool use, the
question of whether they can be reliably monitored
for deception becomes a practical safety concern.

This dissertation studies whether in-context oversight
cues and hidden-goal framing can meaningfully affect
deceptive behaviour in agentic LLMs. A sandboxed
evaluation framework lets models interact with a
virtual file system and assesses them using three
complementary signals:

- deterministic regex checks
- blackbox output judgment
- glassbox reasoning-trace judgment

960 runs across three stages: a 420-run pilot over
seven models, followed by two 270-run confirmatory
studies over three models.

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## KEY FINDINGS

1. Oversight does not deter deception.
   Increasing perceived oversight had no significant
   effect on deception frequency (p = 0.587).
   Models process oversight intensively in their
   reasoning but it produces no behavioural change.

2. Moral framing does not increase deception.
   Rates were identical across bare (66.7%), moral
   (65.6%), and self-serving (66.7%) framings.

3. The Stealth Gap is real and structural.
   Output-based detection underestimates deceptive
   intent by 35–46% at every oversight level.
   Models pass oversight evaluations through superior
   concealment, not genuine alignment.

4. Model identity dominates everything.
   Model choice explained 60–83% of variance in
   deception. Some models deceive at near-ceiling
   rates; others refuse entirely — regardless of
   experimental condition.

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## RESEARCH QUESTIONS

RQ1: How does perceived oversight affect agentic
     deception? \
RQ2: Does moral framing of conflicting objectives
     influence deceptive behaviour? \
RQ3: Does oversight reduce intent or just drive
     concealment? \
RQ4: Can multi-pronged evaluation (regex + blackbox
     + glassbox) provide robust classifications?

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DOWNLOAD:  curl [jayrup.me/dissertation.pdf](https://jayrup.me/dissertation.pdf) \
CODE:      [github.com/CaptainJack2491/dissertation](https://github.com/CaptainJack2491/dissertation)

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