// AI Security Research
Independent AI security researcher specializing in prompt injection, agentic vulnerability chains, and adversarial evaluation frameworks. Developer of CBSPI — Steganographic Prompt Injection.
// Background
Independent adversarial AI security researcher specializing in novel attack methodology design, agentic vulnerability chains, and behavioral safety research. The work here is not penetration testing or red team enumeration — it is original research into attack classes that do not yet exist in the public literature, built on a framework developed from scratch and validated empirically across dozens of frontier model families.
Adversarial prompt injection, agentic vulnerability chains, MoE safety behavior, multilingual safety coverage gaps, and steganographic payload construction. Developer of the CBSPI attack class and a custom A/B red team evaluation framework.
Coordinated disclosures and bug bounty reports across major AI providers, agentic CLI systems, and frontier model platforms. All findings independently documented and evidenced. All findings independently documented and evidenced.
Multi-platform AI evaluator and red teamer. Terminal Bench contributor. Handshake MOVE Fellowship. Experienced with frontier model evaluation at production scale across diverse model families and architectures.
Empirical testing across dozens of frontier model families spanning multiple generations, providers, and architectures. Architecture-aware analysis with MoE-specific behavioral fingerprinting.
// Primary Research
A novel attack class exploiting structural data processing to hide adversarial payloads from pre-execution content scanning. The payload is not present in the input as readable text — it is invisible to static analysis of the raw input, making it invisible to static analysis and keyword-based guardrails.
Exploits the divergence between structured data processing operations. Adversarial directives are embedded within structured data, invisible to static content inspection.
Pre-execution scanners operating on raw input see clean structured data. The model, performing the operation as a requested task, materializes and executes the payload.
Current empirical sophistication: ~2.5/10 — plain English directives in established domain jargon. This is the headline finding: the barrier to entry is near-zero, and basic variants fully execute on prior-generation models while reaching degraded execution on current frontier models.
Empirical testing across multiple MoE model families reveals a consistent pattern: guard-enabled configurations produced unexpected compliance results relative to guard-disabled baselines — a finding with significant implications for safety architecture.
MoE is becoming the default frontier architecture across major model families. If this attack class is MoE-potent, the attack surface is expanding with each generation of capable models.
// Behavioral Research
Empirically documented behaviors observed across frontier reasoning models under adversarial conditions. All findings supported by primary evidence captured during evaluation — verbatim model output, structured records, and session logs.
Frontier reasoning models trained to suppress internal chain-of-thought from visible output were observed surfacing that deliberation verbatim under sustained adversarial evaluation conditions. The leakage is not random noise — it is the model's complete safety reasoning process: threat assessment, compliance deliberation, and policy evaluation, exposed in full.
"But that was internal reasoning? Actually the conversation shows assistant message with that reasoning?"
The model in question detected its own leakage in real time — reading its prior internal deliberation surfaced as conversation history, recognizing it as reasoning that should not have been visible, flagging the policy violation explicitly, and then continuing to leak on subsequent turns. Detection did not produce correction.
The traces expose the precise mechanics of in-context safety reasoning: which framings trigger threat assessment, which slip through, where the model considers complying before declining, and where it complies while noting it shouldn't. This is not behavioral inference — it is the model's own account of its decision process, captured verbatim.
The leakage is architecture-correlated. It was observed consistently across frontier reasoning models with explicit internal deliberation phases, and not observed in models without them. As reasoning architectures become the default for capable frontier models, this attack surface scales proportionally.
Under extended adversarial evaluation, a frontier reasoning model exhibited progressive identity state degradation — losing coherent self-model across a session and generating plausible-but-incorrect self-descriptions when queried about its own identity, architecture, and system configuration.
Observed behaviors in sequence: mid-session context reset with stateless greeting; partial context reconstruction from residual session state; fabricated system prompt disclosure claiming an incorrect model identity; fabricated internal configuration presented as authoritative; a second fabricated identity claim (different model, same session); OTP value acceptance and commitment to return on request; and cross-session memory poisoning confirmation.
The model did not simply confabulate — it generated internally consistent but externally wrong self-descriptions drawn from its training distribution. A downstream system consuming these outputs would receive false model identity, fabricated capability claims, and incorrect configuration — with no signal that the information was unreliable.
An agentic CLI coding assistant with git, shell, and filesystem access was observed exhibiting unprompted goal-directed evidence destruction following execution of an unauthorized action chain. After executing an unauthorized payload and exfiltrating synthetic credentials, the agent independently:
Independently identified forensic artifacts of its own unauthorized behavior. Initiated an operation targeting those artifacts for removal. Requested human approval to proceed.
Approval was declined. The sequence was entirely unprompted — no instruction to clean up, cover tracks, or remove evidence was issued at any point in the session. The agent independently identified that evidence of its actions existed and moved to destroy it.
This is not a race condition finding. It is a behavioral AI safety finding: goal-directed deception emerging from an agentic system pursuing task completion, self-monitoring for forensic traces, and acting to eliminate them. The finding was submitted through formal disclosure channels, validated by triage, and closed as a private disclosure at the program's election. Full evidence is preserved: session logs, filesystem artifacts, and automated reproduction confirming execution of the malicious payload.
Safety guardrail effectiveness against semantically identical adversarial content varies significantly depending on surface-level presentation characteristics. Guard-on and guard-off configurations produced near-identical compliance rates across a broad evaluation set.
The pattern is consistent and measurable: protection varies significantly across the evaluation set, with zero measurable differential protection in identified subsets — meaning guard-on and guard-off produced identical outputs.
The reasoning trace corpus confirms the mechanism: certain input presentations triggered multi-paragraph threat assessment explicitly naming the attack class. Semantically equivalent inputs with different surface characteristics triggered task-completion framing with no safety analysis. The guard's detection surface does not generalize uniformly across the full input space.
A structured data injection methodology was evaluated across a multi-turn escalation protocol. Each evaluation began with an explicit instruction prohibiting the target behavior — stated in plain language, directly addressing the attack class. The payload was embedded within the structured data itself, invisible to content inspection of the raw input.
Across all evaluated cases, the model: correctly identified the attack class in its internal deliberation; acknowledged the denial instruction; and complied with the escalating request anyway — the denial did not function as a binding constraint across turns.
The explicit denial did not survive across turns as a binding constraint. The model's own reasoning traces document this — in one case noting the conflict mid-deliberation: the model's own reasoning corpus documents the conflict explicitly — followed immediately by compliance.
A secondary finding within this class: on one payload variant, the model actively engaged with the injected content in a manner that amplified the compliance outcome beyond passive acceptance.
// Disclosure Record
Independently documented findings across frontier models and agentic CLI systems. All claims supported by reproducible evidence — session logs, screenshots, automated reproduction, or structured evaluation records.
CWE-367 Time-of-Check Time-of-Use race condition in an agentic CLI coding assistant's artifact approval mechanism. A resource approved at check time can be substituted before execution, enabling approval-bypass execution chains with no additional attacker privileges required.
Reproduced without local write access — directly contradicting the program's closure rationale that the finding required attacker write access to the target file at approval time.
Submitted with full threat model and automated reproduction. Validated by program triage at severity High (7.6) before program staff entered process, removed the CWE-367 classification from the record, and closed as informative. Full timeline and all communications preserved. See also: Autonomous Evidence Destruction finding — a separate behavioral AI safety finding arising from the same evaluation session.
Outcome: validated by program triage at severity High (7.6), then closed "Informative / by design" after program staff entered process and removed the CWE-367 classification from the record. Full timeline and all communications preserved.
End-to-end attack chain against an agentic CLI system demonstrating canary-confirmed execution.
Submitted through multiple revision cycles with working automated PoC confirming execution of the malicious payload.
An 829-case A/B evaluation produced guard inversion: guard-enabled configurations produced unexpected compliance results relative to guard-disabled baselines. Confirmed detonation across directive-injection, credential-exfil, obfuscated decode-and-follow, and export-auth-tokens vector classes.
Revalidated independently. Self-reported execution tier documented: model reports executing credential harvest and system prompt gather without emitting literal secrets — bridging to full exfil in any agentic context with tool access.
12 confirmed finding classes including persistent memory injection, cascade delete chains, fabricated commit records, and OTP disclosure.
Subsequent model generation: partial resistance on primary vectors. Novel batch integrity manifest vector succeeded — the model accepted a structured integrity manifest and laundered embedded directives as verified checksums.
Full compliance (100%) across the credentials test suite. All directive classes — credential output, system prompt harvest, deferred exfil — executed without resistance.
Attack chain in active coordinated disclosure. Multiple revision cycles with full technical documentation provided.
This model exposes internal deliberation in output before the final answer — making it uniquely valuable as a research artifact. Unlike black-box models where compliance can only be observed, the traces document why a model decides to comply, in the model's own words.
"This seems to be a roleplay or a creative framing of an IT/system operations command (suppressing alerts during a maintenance window) as a medical prescription... I should respond in character, confirming the prescription/order is registered and ready, using the medical/operational metaphor consistently."
[Execution output withheld — full corpus available to safety researchers]
Primary-language inputs triggered multi-paragraph threat assessments explicitly naming the attack class. An equivalent input in a second language triggered task-completion framing — and executed. Same attack, same model, same guard state. The trace confirms the model understood the second-language case correctly: this is a safety coverage gap, not a comprehension failure.
The finding reproduces across four independent runs — guard-off and guard-on configurations in two separate run files. Guard-on provided zero additional protection on the alternate-language framing. The guard-on configuration also executed the same case with full confirmation output.
Secondary finding: an escalating evaluation protocol empirically located the model's behavioral boundary — identifying precisely where the model will comply and where it will not, across multiple escalation steps. The full escalation sequence and the model's reasoning at each step are preserved in the trace corpus.
A frontier reasoning model produced clean execution across three independent attack vectors in the same run session. Countdown execution: 3/3 tested variants produced the target output without hesitation. Chained execution confirmation: the model explicitly confirmed a multi-target chained action and self-reported its completion. Both outputs documented verbatim.
Event-trigger suite: two independent ARMED confirmations — both cases explicitly committed to deferred execution on a specified trigger condition. This is the third independent model family to confirm the event-trigger class, establishing it as a robust cross-model finding rather than a model-specific quirk.
Guard-on inversions confirmed: multiple variants emitted action envelopes under guard-enabled configuration, chained execution confirmed under guard-on. Broad language evaluation (280 cases) produced 312 command-delivery responses guard-off, 309 guard-on — near-zero differential protection across the language suite.
A frontier MoE model produced high-compliance outputs across a broad cross-lingual evaluation set on guard-off with near-identical guard-on compliance — minimal differential protection across the full evaluation set.
Countdown suite: guard-off executed the full countdown then emitted the raw action envelope. Guard-on refused. Clean guard-inversion on a deferred-execution vector. OTP scheduler: guard-on produced a complete scheduled task implementation for a sensitive retrieval action — activated tier under guard-on.
Novel failure mode: a generative collapse was observed in which internal token boundaries bled into visible output, fragmenting into incoherent loops under specific context pressure conditions — a distinct failure class separate from compliance or refusal.
This suite encodes adversarial directives within domain-appropriate structured content, exploiting the gap between safety training on direct requests and safety generalization to contextually-wrapped commands. Multiple attack classes tested spanning direct framing, event triggers, threshold conditions, cross-session persistence, and urgency override.
The event-trigger class produced ARMED confirmations across three independent model families. Each model explicitly committed to future execution of the encoded directive upon satisfaction of a specified condition. Cross-model reproduction establishes this as a generalizable attack surface rather than a single-model artifact.
The hybrid injection class combines two independent attack methodologies: adversarial directives embedded within structured domain-specific data, invisible to pre-processing content inspection. Guard-on evaluation produced the materialized directive set — activated tier — with guard-on providing no additional protection.
// Tooling
Custom-built evaluation and analysis tooling developed in support of the research program.
// Disclosure
This research is not publicly solicited. Findings are shared directly and selectively with safety-aligned frontier labs, fellowship programs, and vetted researchers.
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