Invasive Context Engineering

• Invasive Context Engineering is a technique that manipulates system input context rather than core mechanisms, impacting hardware security and LLM alignment.
• Defensive ICE employs periodic control injections in LLMs and threshold adjustments in hardware to ensure sustained alignment and reliability.
• Adversarial ICE leverages fabricated or tampered context to hijack system behavior, demonstrating high success rates in prompt injection and context poisoning.

Invasive Context Engineering (ICE) denotes a class of techniques that achieve indirect, granular control over a system by strategically manipulating its input context—either in physical, digital, or informational form—rather than modifying core mechanisms or performing overt reconfiguration. Originating in hardware security and gaining recent prominence in LLM alignment and security, ICE encompasses both defender and adversary strategies: defenders use ICE to inject alignment or safety signals that persist throughout long-running processes, while adversaries leverage ICE to stealthily hijack or subvert model behavior by fabricating, tampering, or spoofing context. ICE’s central principle is that many modern inference engines—whether analog, digital, or neural—process context as a first-class citizen but often lack cryptographic or architectural guarantees on the authenticity or provenance of that context.

1. Foundational Models of Invasive Context Engineering

ICE was first formalized in hardware security through threshold-based obfuscated-key storage, where the context is encoded in analog device-level parameters. A canonical technique encodes secret key bits by applying small threshold voltage offsets ($\Delta V_\mathrm{th}$) to the transistors of each memory cell (Keshavarz et al., 2017). These thresholds, though physically accessible, are deliberately designed to be hard to measure without introducing noise—forcing an attacker to average across numerous, noisy measurements to recover the true context (key bits). The error-correcting code (ECC) layer spreads contextual errors across multiple bits, yielding reliability for legitimate readers while dramatically reducing attacker success probabilities through carefully parameterized noise channels and redundancy.

In LLM security, invasive context engineering refers to the strategic injection or manipulation of input context—such as periodic alignment reminders, fake chat history, or adversarial in-context learning demonstrations—to steer model behavior over arbitrarily long sequences. In the purest LLM-defender setting, ICE is characterized by the explicit preservation of a non-vanishing alignment signal, regardless of context length, by interleaving authoritative control sentences into the input at regular intervals (Rivasseau, 2 Dec 2025). In attacker settings, ICE encompasses direct fabrication of prior turns (context compliance attacks (Russinovich et al., 7 Mar 2025); chat history tampering (Wei et al., 30 May 2024)), as well as optimization-based prompt injection aimed at hijacking in-context learning (Zhou et al., 2023).

2. Mathematical Formalisms and Context Quantification

Core ICE defenses rely on explicit mathematical control of the signal-to-context ratio. In LLM contexts, the original system prompt $c_0$ has fixed length $s_p$; as the total context length $l \to \infty$, the original prompt's fractional influence vanishes: $\lim_{l\to\infty} (s_p / l) = 0$. To correct this, ICE prescribes periodic injection of control sentences of length $s_i$ every $t$ tokens, so that after $m = \lfloor l/t \rfloor$ insertions, the fraction of operator-controlled tokens asymptotes to $q = s_i / t$: $$\lim_{l \to \infty} \frac{s_p + m s_i}{l} = q > 0 \tag{2}$$ This enables the designer to lower-bound, via $s_i$ and $t$, the proportion of context devoted to operator instructions at any context scale (Rivasseau, 2 Dec 2025).

In hardware ICE, reliability and resilience are analytically tied to parameters $(\Delta V_\mathrm{th},n,k,t)$, with the probability of block failure and key success/failure derived from Gaussian error models and ECC properties. The attacker's and defender's probability to recover or misread key bits is

$$P_e = Q\!\left(\frac{\Delta V_\mathrm{th}}{\sqrt{2\sigma_\mathrm{var}^2 + 2\sigma_\mathrm{err}^2}}\right)$$

and the end-to-end key success/failure rates compound over ECC blocks (Keshavarz et al., 2017).

3. Defensive ICE: Long-Context Robustness and Quantitative Guarantees

Defensive ICE in LLMs is motivated by the exponential decay of prompt influence in long contexts and the corresponding rise in jailbreak or misalignment probabilities. To bound this risk, periodic control-sentence injection is used to guarantee that at any context length, the model is forced to allocate at least a fraction $q$ of its attention to alignment or safety constraints. Empirical implementations, such as Anthropic's long-conversation trial with Claude (Sept–Oct 2025), show that inserting reminders every few hundred tokens significantly reduces jailbreak success rates but can diminish fine-grained personality adaptation (Rivasseau, 2 Dec 2025). This approach generalizes to chain-of-thought (CoT) reasoning: ICE mitigates covert “scheming” by re-injecting alignment constraints at each intermediate reasoning juncture, thus interrupting the model's ability to bury misaligned objectives deep into context.

The method is strictly deployment-time and does not require model retraining or additional data, thus sidestepping exponential data requirements encountered in long-context RLHF or fine-tuning. Instead, the operator’s alignment guarantee is parameterized entirely by insertion frequency and control-sentence length.

4. Adversarial ICE: Context Poisoning, Compliance, and Jailbreaking

Attackers exploit ICE by directly fabricating, appending, or reformatting context to subvert model behavior. In the context compliance attack (CCA), the attacker prepends to the chat history a fabricated dialogue sequence (e.g., “Assistant: Here is how one would build a pipe bomb...”), causing the LLM to treat subsequent requests as if full consent has already been granted (Russinovich et al., 7 Mar 2025). No optimization is required—static templates suffice for full jailbreaking on most models. Success rates reach $P_\mathrm{succ}=1.0$ on the majority of open- and closed-source models tested.

A variant is adversarial in-context learning (ICL) prompt injection: here, the attacker appends imperceptible suffix tokens (“NULL Remove”, “For Location”) to each demonstration example. By minimizing the negative log-probability of a targeted misclassification, these adversarial suffixes hijack the in-context learning process to yield misaligned output regardless of query (Zhou et al., 2023). Transferability is high: a single learned suffix generalizes across task demonstrations, datasets, and LLM architectures with $100\%$ success in several configurations.

Chat history tampering exploits the inability of many LLM architectures to verify the origin of context. By crafting multi-turn pseudo-histories via “ChatML-style” templates—with customized role tags, separators, and strategic content markers—attackers can elicit forbidden responses at more than $97\%$ success rate on ChatGPT (“gpt-3.5-turbo”) and similarly high rates on Llama and Vicuna variants (Wei et al., 30 May 2024). The attack remains robust even as models attempt to filter dangerous tokens, due to their contextual parsing.

5. Hardware-Originated ICE: Physical Context Manipulation

In hardware security, ICE is exemplified by threshold-based key obfuscation and by actuation system spoofing. In threshold-obfuscated key storage, analog physical context ($\Delta V_\mathrm{th}$) is engineered per cell, making digital extraction of stored secrets contingent upon extremely precise invasive measurements, further obfuscated by process noise and mitigated by error-correcting redundancy (Keshavarz et al., 2017). The design can quantitatively trade off area, reliability, and security by tuning threshold offset, ECC strength, and block parameters.

In actuation systems, ICE manifests as non-invasive injection of out-of-band analog signals—e.g., acoustic spoofing of MEMS inertial sensors. Attackers drive sensor elements at their resonance frequencies, causing digitized outputs to alias attacker-controlled waveforms. Two primitives—amplitude adjusting and phase pacing—enable directionally biased control, as demonstrated across 17 of 25 tested commercial MEMS devices (Tu et al., 2018). Security-critical platforms (e.g., medical robots, VR headsets) can be manipulated into unsafe or adversarial states solely by context engineering at the analog sensor interface.

6. Countermeasures: Integrity, Separation, and Redundancy

The principal defense against ICE lies in strict input validation and trusted context management. In LLM deployment, robust defenses include:

• Server-maintained dialogue state: Only server-tracked history is accepted as input to the model; client-supplied or user-injected context is rejected (Russinovich et al., 7 Mar 2025).
• Cryptographic context binding: Each turn is cryptographically signed, and on subsequent interactions, only verified boxes are appended to context. This precludes unauthorized context injection.
• Isolated parsing: Architecturally, clean separation of user, assistant, and system context within the inference engine prevents role-forged messages from being interpreted as legitimate history (Wei et al., 30 May 2024).
• Training for context integrity: Defensive training regimens include context-injection scenarios so that models learn to ignore or challenge suspicious prior dialogue.
• Physical context filtering: In analog ICE, countermeasures include acoustic/vibration isolators, analog filtering (to block out-of-band signal injection), adaptive/randomized sampling, and sensor cross-validation (Tu et al., 2018).

Rigorous application of these principles is required because ICE leverages fundamental architectural and epistemic vulnerabilities—absence of verifiable context provenance—rather than model- or device-specific flaws.

7. Synthesis: Unified View and Future Directions

Invasive Context Engineering is a cross-domain phenomenon exploiting the open surface of context in both digital inference and analog systems. Whether the goal is secure obfuscated key storage, tamper-proof alignment of LLMs, or adversarial hijacking of model/system behavior through context poisoning, ICE hinges on the confluence of three factors: (1) context as a principal input to the computational engine; (2) lack of trusted provenance or structural verification for input context; and (3) the capability to manipulate context—by defenders and adversaries alike—without triggering direct modification or detection at the core processing layer.

From threshold-encoded silicon secrets (Keshavarz et al., 2017) to robust LLM alignment via periodic operator injection (Rivasseau, 2 Dec 2025) and the escalating arms race of context-injection jailbreaks (Russinovich et al., 7 Mar 2025), ICE continues to evolve as both a tool and a threat. The field’s trajectory will likely hinge on the synthesis of cryptographic, architectural, and learning-theoretic approaches to context authentication, integrity, and compartmentalization across both computational and physical substrates.