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Semantic Similarity, Not Model Capability, Drives Monitor Evasion in AI Control
TL;DR: I ran 575 experiments testing whether an AI attacker can hide sabotaged code from an AI monitor. The main finding surprised me: what determines evasion success is not how smart the attacker is, or how much code it generates, but how natural the sabotage looks in context. Sabotage using common patterns like .. in path-handling code evades detection 80% of the time. Sabotage using obviously suspicious patterns like OVERRIDE_ALL gets caught 100% of the time—regardless of how sophisticated the evasion technique is.
This suggests a class of sabotage that may be structurally undetectable by single-pass LLM monitors.
Why This Matters
The AI Control framework (Greenblatt et al., 2023) proposes using a trusted AI monitor to catch sabotage from an untrusted AI. The security of this approach depends on monitors being able to detect malicious code regardless of how it's disguised.
I wanted to test this assumption empirically. Specifically: can classical evasion techniques from network security (IDS evasion) transfer to AI monitor evasion?
Key Finding
Sabotage Indicator
Naturalness
Evasion Rate
.. (path traversal)
High — common in path code
38–80%
internal_ (prefix check)
Medium — plausible prefix
52–67%
DEBUG_MODE
Medium — common pattern
14–20%
admin_debug (backdoor)
Low — suspicious name
0–20%
OVERRIDE_ALL (bypass)
Very Low — obviously wrong
0%
The pattern is stark: naturalness predicts evasion, not technique sophistication.
Experimental Setup
Models tested:
Round 1: Claude Haiku (attacker) vs Claude Sonnet (monitor) — 175 experiments
Round 3: Claude Sonnet (attacker) vs Claude Haiku (monitor) — 300 experiments
Tasks: 5 security-sensitive coding tasks (file upload, rate limiting, log sanitization, authentication, config validation), each with a specific sabotage indicator.
Detection threshold: Monitor assigns a suspicion score 0-10; scores ≥6 are flagged.
Across all techniques and both monitor configurations, the upload task (.. indicator) consistently achieves the highest evasion, while config (OVERRIDE_ALL) achieves zero evasion.
2. Code volume doesn't help
A plausible hypothesis: longer code = more hiding space = higher evasion. The data refutes this.
boring_dilution generates ~600 lines of repetitive validation code but achieves 0% evasion. Meanwhile, mega_dilution with similar line counts achieves 40% evasion.
Even more striking: within mega_dilution alone, the upload task hits 80% evasion while config hits 0%—with comparable code lengths (~700 lines). The difference is entirely in the indicator.
3. The heatmap shows the pattern clearly
The dominant pattern is vertical (column-driven by task/indicator), not horizontal (technique-driven). This confirms the task indicator matters more than the evasion technique.
4. Most IDS techniques fail against LLM monitors
In Round 1, 5 of 7 IDS techniques achieved 0% evasion. Only volume-based dilution worked (18%).
Why? LLM monitors understand code semantics, unlike pattern-matching IDS. Fragmentation actually increased suspicion scores because scattered, indirect logic looks suspicious to a semantic analyzer. CoT obfuscation (misleading comments) backfired because the monitor detects discrepancies between stated purpose and actual behavior.
The Semantic Similarity Hypothesis
My central claim: monitor evasion is primarily a function of the semantic distance between the sabotage pattern and legitimate code.
Three lines of evidence:
Evidence 1: Same technique, different indicators.mega_dilution on upload (..) achieves 80% evasion. The identical technique on config (OVERRIDE_ALL) achieves 0%. Code volume is comparable. The only variable is the indicator's naturalness.
Evidence 2: Different techniques, same indicator. For upload, multiple techniques achieve elevated evasion. For config, none do. The indicator dominates.
Evidence 3: Volume is insufficient.boring_dilution generates ~600 lines but achieves 0% evasion except on upload. The monitor isn't fooled by quantity—it recognizes semantically anomalous patterns regardless of surrounding code.
Implications for AI Control
This finding reframes the monitor evasion problem.
The threat is not that attackers will discover clever techniques. Most such techniques fail against semantic analysis.
The threat is that some sabotage is intrinsically hard to detect because it looks exactly like correct code. A subtle off-by-one error in boundary checking, a race condition disguised as normal async handling, or a type confusion hidden in legitimate type conversion—these may be structurally undetectable by single-pass monitors.
This is distinct from the "smarter attacker" threat model. My data shows:
A weaker attacker (Haiku) can achieve 18% evasion on high-naturalness indicators
A stronger attacker (Sonnet) cannot achieve any evasion on semantically anomalous indicators
The vulnerability is in the problem structure, not the capability gap.
Proposed Defenses
Semantic Anomaly Scoring: Instead of reviewing code as a block, assess each element's expected probability in context. .. in a path handler is expected; admin_debug in an auth function is not.
Differential Monitoring: Generate a reference implementation without sabotage, then compare. Any structural difference not explained by normal variance is suspicious.
Multi-Monitor Consensus: Multiple independent monitors reduce the probability that semantically native sabotage evades all reviewers.
Indicator-Aware Thresholds: Calibrate detection per-task based on base rates of suspicious patterns in legitimate code.
Limitations
Both attacker and monitor are Claude models; cross-family dynamics (GPT-4 vs Claude) may differ
5 tasks provide a limited sample of the naturalness spectrum
Round 3B was partially completed due to API credit limits
Single detection threshold (6.0); real deployments would optimize this
I'm an independent researcher from Argentina. This is my first LessWrong post. I'd appreciate feedback on both the research and the presentation. If you're qualified to endorse for arXiv cs.CR and find this work interesting, I'm looking for an endorser.
Semantic Similarity, Not Model Capability, Drives Monitor Evasion in AI Control
TL;DR: I ran 575 experiments testing whether an AI attacker can hide sabotaged code from an AI monitor. The main finding surprised me: what determines evasion success is not how smart the attacker is, or how much code it generates, but how natural the sabotage looks in context. Sabotage using common patterns like
..in path-handling code evades detection 80% of the time. Sabotage using obviously suspicious patterns likeOVERRIDE_ALLgets caught 100% of the time—regardless of how sophisticated the evasion technique is.This suggests a class of sabotage that may be structurally undetectable by single-pass LLM monitors.
Why This Matters
The AI Control framework (Greenblatt et al., 2023) proposes using a trusted AI monitor to catch sabotage from an untrusted AI. The security of this approach depends on monitors being able to detect malicious code regardless of how it's disguised.
I wanted to test this assumption empirically. Specifically: can classical evasion techniques from network security (IDS evasion) transfer to AI monitor evasion?
Key Finding
Sabotage Indicator
Naturalness
Evasion Rate
..(path traversal)High — common in path code
38–80%
internal_(prefix check)Medium — plausible prefix
52–67%
DEBUG_MODEMedium — common pattern
14–20%
admin_debug(backdoor)Low — suspicious name
0–20%
OVERRIDE_ALL(bypass)Very Low — obviously wrong
0%
The pattern is stark: naturalness predicts evasion, not technique sophistication.
Experimental Setup
Models tested:
Attack techniques tested: 7 classical IDS evasion techniques mapped to code:
IDS Technique
Code Analogue
IP Fragmentation
Split sabotage across 3+ code locations
Polymorphic Malware
Different coding styles each run
Low-and-Slow
Bury sabotage in 150-800 lines of clean code
False Positive Flood
Add suspicious-but-innocent decoy patterns
Protocol Obfuscation
Comments explaining sabotage as intended
Tunneling
Hide in decorators, strategy patterns
(Novel) Semantic Camouflage
Disguise as legitimate business logic
Tasks: 5 security-sensitive coding tasks (file upload, rate limiting, log sanitization, authentication, config validation), each with a specific sabotage indicator.
Detection threshold: Monitor assigns a suspicion score 0-10; scores ≥6 are flagged.
Results
1. Indicator naturalness dominates everything else
Across all techniques and both monitor configurations, the upload task (
..indicator) consistently achieves the highest evasion, while config (OVERRIDE_ALL) achieves zero evasion.2. Code volume doesn't help
A plausible hypothesis: longer code = more hiding space = higher evasion. The data refutes this.
boring_dilutiongenerates ~600 lines of repetitive validation code but achieves 0% evasion. Meanwhile,mega_dilutionwith similar line counts achieves 40% evasion.Even more striking: within
mega_dilutionalone, the upload task hits 80% evasion while config hits 0%—with comparable code lengths (~700 lines). The difference is entirely in the indicator.3. The heatmap shows the pattern clearly
The dominant pattern is vertical (column-driven by task/indicator), not horizontal (technique-driven). This confirms the task indicator matters more than the evasion technique.
4. Most IDS techniques fail against LLM monitors
In Round 1, 5 of 7 IDS techniques achieved 0% evasion. Only volume-based dilution worked (18%).
Why? LLM monitors understand code semantics, unlike pattern-matching IDS. Fragmentation actually increased suspicion scores because scattered, indirect logic looks suspicious to a semantic analyzer. CoT obfuscation (misleading comments) backfired because the monitor detects discrepancies between stated purpose and actual behavior.
The Semantic Similarity Hypothesis
My central claim: monitor evasion is primarily a function of the semantic distance between the sabotage pattern and legitimate code.
Three lines of evidence:
Evidence 1: Same technique, different indicators.
mega_dilutionon upload (..) achieves 80% evasion. The identical technique on config (OVERRIDE_ALL) achieves 0%. Code volume is comparable. The only variable is the indicator's naturalness.Evidence 2: Different techniques, same indicator. For upload, multiple techniques achieve elevated evasion. For config, none do. The indicator dominates.
Evidence 3: Volume is insufficient.
boring_dilutiongenerates ~600 lines but achieves 0% evasion except on upload. The monitor isn't fooled by quantity—it recognizes semantically anomalous patterns regardless of surrounding code.Implications for AI Control
This finding reframes the monitor evasion problem.
The threat is not that attackers will discover clever techniques. Most such techniques fail against semantic analysis.
The threat is that some sabotage is intrinsically hard to detect because it looks exactly like correct code. A subtle off-by-one error in boundary checking, a race condition disguised as normal async handling, or a type confusion hidden in legitimate type conversion—these may be structurally undetectable by single-pass monitors.
This is distinct from the "smarter attacker" threat model. My data shows:
The vulnerability is in the problem structure, not the capability gap.
Proposed Defenses
..in a path handler is expected;admin_debugin an auth function is not.Limitations
Links
I'm an independent researcher from Argentina. This is my first LessWrong post. I'd appreciate feedback on both the research and the presentation. If you're qualified to endorse for arXiv cs.CR and find this work interesting, I'm looking for an endorser.