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---
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
--------------------------------------------------
## 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.
--------------------------------------------------
## 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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