Restaking and Shared Security: Risk, Slashing, and Market Microstructure of LSTs and LRTs

Restaking has moved from niche curiosity to a central narrative in Ethereum and crypto infrastructure. By letting the same staked capital secure multiple protocols, it promises:

• Higher capital efficiency and yields for stakers.
• “Ethereum-grade” security for new networks without bootstrapping their own validator sets.

That efficiency comes with a new, still underpriced set of risks. Restaking links:

• Multiple protocols and AVSs.
• Validator operators and their infrastructure.
• Liquid staking and restaking tokens (LSTs and LRTs).

Shared collateral and shared slashing conditions create correlated failure modes, while LRT-driven leverage makes small price moves capable of triggering large deleveraging cascades.

This article treats restaking and shared security as a financial primitive and focuses on:

• Technical architecture of EigenLayer-style systems and AVSs.
• How restaking reshapes validator and LST/LRT risk.
• How correlated slashing and AVS failures can cascade.
• LRT market microstructure: premia/discounts, liquidity, leverage.
• The competitive landscape (EigenLayer, Symbiotic, Karak).
• Bull, base, and bear scenarios.

The lens is mechanism design, on-chain and market data where available, and explicit downside analysis rather than yield marketing.

1. Fundamentals: Restaking Architecture and Shared Security

1.1 Why Restaking Exists

Traditional proof-of-stake has a built-in inefficiency. New protocols that want economic security must either:

• Launch a new token and validator set, fragmenting security into many small caps; or
• Pay heavily in token emissions to attract validators, often with inflationary, unsustainable economics.

EigenLayer’s answer is pooled or shared security. Instead of every protocol bootstrapping a fresh validator set and security token, they rent security from Ethereum’s existing stake. Restakers opt into additional slashing conditions in exchange for extra yield paid by the protocols they secure.

This shifts from “one stake, one protocol” to “one stake, many protocols,” treating security as a composable resource.

1.2 Core Actors in EigenLayer-Style Systems

EigenLayer’s model centers on four main actors:

• Restakers
  Deposit native staked ETH or LSTs into EigenLayer. They keep ownership of the underlying stake but accept extra slashing conditions defined by AVSs in return for additional rewards.

• Operators
  Infrastructure providers who register with EigenLayer, receive delegated stake, and run AVS software. They are the entities that get slashed and share rewards with restakers.

• Actively Validated Services (AVSs)
  Protocols that consume security: DA layers, oracle networks, shared sequencers, middleware, cross-chain services, and similar systems. Each AVS defines its own rules, reward flows, and slashing conditions.

• End-users and applications
  Use AVS services (e.g., DA, oracles) and benefit indirectly from Ethereum-level security without interacting with restaking directly.

Economically, this is a marketplace. AVSs bid for restaked ETH by offering rewards. Restakers and operators allocate capital and operational capacity to AVSs with the best perceived risk-adjusted returns.

1.3 Decoupling Rewards and Collateral

EigenLayer separates the asset being slashed from the asset paying rewards:

• In standard PoS, a protocol’s native token is both:

  • The collateral at risk; and
  • The asset being emitted as rewards.

  That forces new protocols to emit large volumes of often-illiquid, inflationary tokens to attract validators.

• In restaking, AVSs can pay in whatever asset fits their economics (ETH, stablecoins, or their own token). The slashing collateral remains ETH or ETH-referenced LSTs, with deep liquidity and established value.

Security becomes more like a service or debt instrument: AVSs pay for it in flexible assets while tapping Ethereum’s existing capital base as collateral.

1.4 Security Composability and Unique Stake Allocation

A core early concern was “one slash nukes everything”: a slashing event on a single AVS could wipe out an operator’s entire restaked position and trigger contagion across unrelated services.

EigenLayer’s Unique Stake Allocation mitigates this. An operator with 100 ETH restaked can explicitly allocate portions to specific AVSs, for example:

• 30 ETH → low-risk DA AVS
• 40 ETH → mid-risk oracle AVS
• 30 ETH → high-risk sequencer AVS

If the sequencer AVS triggers slashing, only the 30 ETH allocated there is at risk, not the full 100 ETH.

This isolates loss buckets but doesn’t remove correlation:

• Operators often run multiple AVSs on shared infrastructure. A major operational failure (downtime, misconfiguration, key compromise) can still lead to slashing across several AVSs.
• Shared software dependencies (libraries, middleware) can create correlated bugs across AVSs, even where stake is allocated separately.

Unique Stake Allocation changes how losses are distributed; it doesn’t remove the channels that create them.

1.5 Growth Metrics and Collateral Mix

EigenLayer’s early growth shows the appeal of pooled security:

• TVL climbed from around 3 billion USD in March 2024 to about 19.5 billion USD in May 2024, then moderated to roughly 14.5 billion USD later in 2024.
• Around 61% of that TVL came from native staked ETH; the rest from LSTs.
• Lido’s stETH alone made up about 21.5% of EigenLayer’s TVL.

This composition matters:

• A large share of restaked security sits behind LSTs, which can trade away from 1:1 parity with ETH.
• If an LST depegs, the effective economic security backing AVSs is lower than nominal TVL suggests because the market value of collateral falls, even if the raw ETH backing is unchanged.

Competing restaking protocols add more variance:

• Symbiotic launched in early 2025 and reached around 1.5 billion USD in TVL within weeks, signaling strong demand for alternative designs.
• Karak has a smaller but meaningful share (around 2–3%), emphasizing multi-asset restaking rather than ETH-only.

These systems differ in slashing rules, collateral sets, and governance. That can diversify risk but also opens more cross-protocol failure paths.

2. Liquid Staking and Liquid Restaking: Mechanisms and Market Structure

Restaking at scale is driven by liquid staking. LSTs and LRTs are the main entry points for most users, and their behavior under stress is central to systemic risk.

2.1 LSTs: Turning Staked ETH into a DeFi Asset

Ethereum’s native staking design locks 32 ETH per validator with exits processed via a protocol queue. This creates:

• Illiquidity: locked ETH can’t be used elsewhere.
• Exit friction: in high-demand periods, exit queues stretch for days or more.

Liquid staking protocols like Lido address this by:

• Accepting ETH deposits and running validators for users.
• Issuing fungible LSTs (stETH, etc.) that represent claims on staked ETH plus rewards.
• Enabling LST use throughout DeFi (DEXs, lending, derivatives), while ETH stays staked.

LSTs convert static staking positions into composable, yield-bearing assets.

2.2 LRTs: Tokenizing Restaked Portfolios

Liquid restaking tokens add another layer. Instead of tokenizing staked ETH, they tokenize restaked positions that secure AVSs via EigenLayer or similar.

A typical LRT flow:

1. User deposits ETH or LST (e.g., stETH) into an LRT protocol (Ether.fi, Renzo, Kelp DAO, etc.).
2. The protocol stakes/restakes through EigenLayer, delegates to operators, and opts into selected AVSs.
3. The protocol issues an LRT (eETH, ezETH, rsETH, etc.) representing a share of this restaked portfolio.
4. The LRT can be used as collateral or liquidity in DeFi while underlying positions earn:
   • Ethereum staking rewards.
   • AVS rewards.
   • Any protocol incentives or airdrops.

The result is layered yield: base staking + AVS rewards + LRT incentives.

2.3 LRT Market Size and Concentration

The LRT sector grew rapidly in 2024:

• TVL rose from around 300 million USD in January 2024 to over 10 billion USD by May 2024.
• Within EigenLayer’s ecosystem, Ether.fi captured roughly 75% share, with Kelp DAO and Renzo at about 12% and 8.5%.

Concentration cuts both ways:

• It deepens liquidity and DeFi integrations around dominant LRTs.
• It creates single points of failure: a major bug, governance failure, or AVS misconfiguration in the leading LRT protocol would affect a large portion of the restaking stack.

By late 2024 and into 2025, LRT TVL became more cyclical:

• Peaks above 13 billion USD at times.
• Pullbacks toward ~8 billion USD in some venues (e.g., Pendle) as narratives rotated and risk awareness rose.

Composition also shifted:

• Early 2024: concentrated on staking and restaking tokens.
• Later: flows rotated into Bitcoin-related finance, then into stablecoin and fixed-yield strategies.

So LRT flows follow broader narrative and risk cycles, not just pure carry.

2.4 How LSTs and LRTs Try to Stay at Par

For LSTs and LRTs to serve as reliable collateral, they must trade near the value of their backing. Three mechanisms help:

1. Primary redemptions
   Many LSTs and some LRTs allow direct redemption, subject to queues and buffers.
   Example: Lido’s stETH can be redeemed via a withdrawal system backed by an execution-layer reward buffer and withdrawal vaults. That buffer has absorbed large exits, including a 428,000 ETH withdrawal linked to Celsius in May 2023.

2. Secondary market liquidity
   LSTs and LRTs trade on DEXs (and sometimes CEXs). For stETH, the Curve stETH/ETH pool has been the main venue. Deep, balanced pools let users swap in and out near par in normal conditions.

3. Arbitrage
   When market prices diverge from redemption value, arbitrageurs can:

   • Buy discounted tokens and redeem them, or
   • Hedge and hold until redemption.

   This relies on:

   • Sufficient liquidity and reasonable slippage.
   • Tolerable redemption times.
   • Controlled risk of further depegs during the waiting period.

Under stress, these channels slow or clog, and discounts or premia can persist.

2.5 Structural Fragilities: A Four-Part LRT Risk Lens

A useful way to think about LRT resilience, drawing on research such as Gauntlet’s work, is through four dimensions:

1. External Liquidity Depth
   How much size can markets absorb without big price impact (e.g., can average daily volume be traded with <5% slippage)?

   • Newer or smaller LRTs tend to have shallow liquidity relative to TVL.
   • TVL can grow faster than liquidity, making larger positions harder to exit.

2. Liquidity Concentration
   How liquidity is distributed among LPs.

   • For Rocket Pool’s rETH on Balancer, the top three LPs often provided 30–70% of liquidity, occasionally over 90%.
   • Similar concentration patterns are likely in many LRT pools, especially early on.

   When a few LPs dominate, their exit can hollow out liquidity overnight.

3. Withdrawal Queue Mechanics
   Exit times introduce a temporal dimension to liquidity.

   • Ethereum exits validators at a fixed rate per epoch (around 4,700 validators/epoch). When exit demand spikes, backlogs build.
   • In July 2025, about 235,000 ETH sat in the exit queue, implying multi-day delays.
   • During these periods, primary redemptions slow, while secondary prices can fall under sell pressure, leaving holders trapped between illiquid redemptions and discounts on DEXs.

4. DeFi Composability and Leverage
   LSTs and LRTs are widely used as collateral on lending protocols (Aave, Compound, Gearbox, etc.), enabling classic leverage loops:

   • Deposit LST/LRT as collateral.
   • Borrow ETH.
   • Buy more LST/LRT.
   • Repeat.

   This amplifies yield in calm markets but creates a nonlinear response to price moves:

   • Small depegs trigger margin calls.
   • Liquidations dump the depegged asset, deepening the discount.
   • The feedback loop can drive cascades.

Even modest price deviations can drive large systemic effects when leverage is high.

2.6 Case Study: stETH Depeg and Liquidation Cascades

The May–June 2023 stETH depeg illustrates these dynamics:

• Large players (Three Arrows Capital, Celsius, and others) unwound positions, pushing stETH to a persistent discount versus ETH.
• Popular leverage trades (borrowing ETH against stETH on Aave to buy more stETH) broke down as borrowing costs rose with high utilization.
• Whales such as Justin Sun repositioned, and Aave’s WETH borrow rates spiked, pushing leveraged stETH positions toward liquidation.
• Liquidations sold stETH into thinning liquidity, widening the discount, which triggered more liquidations.

Key takeaways:

• Exit queues, variable borrow rates, and whale flows can interact in complex ways.
• Even a “blue-chip” LST with deep liquidity can suffer meaningful and persistent depegs.
• Leverage transforms modest price dislocations into systemic events.

LRTs inherit all of this and add AVS and slashing risk on top.

3. Slashing, Shared Security, and Cryptoeconomic Risk

Slashing is what gives PoS security bite. Restaking extends slashing from base-layer consensus to a wide range of AVSs with diverse and sometimes opaque conditions.

3.1 Slashing in Native Ethereum PoS

On Ethereum, slashing is:

• Narrowly scoped to behaviors that threaten consensus safety and liveness:
  • Double-signing blocks.
  • Conflicting attestations.
• Calibrated:
  • Small penalties and temporary inactivity for minor infractions.
  • Large penalties and ejection for serious offenses.

And crucially:

• Objective: misbehavior is proven cryptographically from on-chain data.
• Protocol-enforced: rules are encoded in Ethereum itself and based on extensive research.

3.2 Slashing in Restaking and AVSs

AVSs define their own slashing rules. EigenLayer distinguishes:

1. Objectively attributable faults
   Provable by cryptographic evidence. Examples:

   • DA layer serving invalid or incomplete data.
   • Sequencer proposing an invalid state transition.

   These can be enforced by smart contracts: submit a valid fault proof, and the stake allocated to that AVS is slashed.

2. Intersubjectively attributable faults (group subjectivity)
   No pure cryptographic proof, but reasonable observers can agree misbehavior happened. Examples:

   • Oracles publishing manipulated prices.
   • Committees failing off-chain duties.

   These require governance or human judgment: a DAO, committee, or multisig decides whether to slash, based on off-chain evidence.

Objective faults are programmable and predictable. Intersubjective faults add governance risk, political risk, and uncertainty around when and how slashing will occur.

3.3 Multiplicative Slashing Exposure for Restakers

Restakers stack slashing conditions:

• A pure Ethereum validator is only exposed to Ethereum’s rules.
• A restaker’s ETH or LST is also exposed to:
  • Each AVS’s slashing conditions.
  • Each AVS’s governance process.
  • Each operator’s operational performance across multiple services.

As restakers opt into more AVSs:

• The chance of some slashing event hitting them rises.
• The loss severity depends on:
  • How much stake is allocated to the affected AVS.
  • That AVS’s penalty parameters.

Economically, restaking turns a simple staking exposure into a portfolio of correlated, partly opaque credit-like exposures to multiple AVSs and operators.

3.4 Operator Risk and Cross-AVS Correlation

Operators are the operational choke points:

• They run the software for multiple AVSs, often on shared infra.
• They manage keys, alerts, and upgrades across a heterogeneous stack.

Correlation channels include:

• Operational failures: downtime, misconfigurations, or network issues can simultaneously break obligations across several AVSs.
• Security incidents: compromised keys or malware can force misbehavior everywhere that operator participates.
• Shared software bugs: issues in libraries, tooling, or middleware used by many AVSs.

Even with Unique Stake Allocation, a major operator incident can translate into multiple partial slashes that add up to large losses for their delegators.

3.5 AVS Design Risk

AVSs themselves are new, with:

• Smart contract risk: bugs in AVS contracts or EigenLayer integration can cause unintended slashing or fund loss.
• Economic design risk: poorly tuned incentives can reward or enable attacks that profit from triggering slashing or manipulating AVS behavior.
• Governance risk: in intersubjective regimes, capture or poor governance decisions can lead to arbitrary or politically driven slashing.

Restakers are effectively underwriting these experimental designs.

4. Cascading Losses and Correlated Risk Scenarios

Restaking’s main systemic risk is tight coupling: many protocols, tokens, and leverage loops share the same collateral and slashing triggers.

4.1 Cascade from a Single AVS Bug

Consider a realistic chain of events:

1. A popular AVS (say, a DA layer with a large share of EigenLayer stake) has a critical bug.
2. The bug makes honest operators look faulty (e.g., failing data availability checks), triggering objective slashing.
3. A large portion of stake allocated to this AVS is slashed.

Immediate effects:

• Restakers see their restaked positions cut.
• LRTs heavily exposed to this AVS have impaired collateral backing.

Secondary effects:

• LRT prices drop relative to ETH, triggering liquidations where LRTs are used as collateral.
• Liquidations sell LRTs into DEX pools, pushing prices down further.
• Arbitrage is constrained by slow or uncertain redemptions and fear of more slashing.

Tertiary effects:

• If the AVS is important infrastructure (e.g., for many rollups), its failure degrades downstream apps and DeFi.
• Confidence in restaking falls, leading to EigenLayer and LRT outflows, which stress exit queues and liquidity.

Even with stake allocation limiting direct losses, market reactions can be nonlinear once leverage and composability kick in.

4.2 Correlated Slashing Across Multiple AVSs

A more severe case involves multi-AVS slashing:

• An operator runs several AVSs on shared infrastructure.
• A configuration error, outage, or exploit breaks obligations on multiple AVSs.
• Each AVS slashes the stake allocated to it.

For a restaker who concentrated delegation in that operator, the experience looks like:

• Several partial slashes adding up to large losses.
• Sudden drops in the value of the LRTs that represent that restaked portfolio.

If a top operator by delegated stake suffers this, the impact spreads across the ecosystem, especially if that operator is core to multiple LRT protocols.

4.3 Exit Queues, DeFi Liquidations, and Liquidity Spirals

Slashing shocks interact with existing frictions:

• Exit queues: More restakers try to exit EigenLayer or unstake Ethereum, lengthening queues and adding redemption uncertainty.
• DeFi liquidations: LRTs and LSTs used as collateral are marked down, pushing loans into undercollateralization and triggering liquidations.
• Liquidity spirals: Liquidations dump the already-depressed asset into thin liquidity, deepening discounts and forcing more liquidations.

Past events like the stETH depeg showed that:

• Borrow rates can spike to extreme levels as utilization nears 100%.
• Leveraged strategies flip from profitable to toxic and are forcibly unwound.
• Large players’ responses can further imbalance liquidity and pricing.

With restaking, slashing adds not just price volatility but permanent capital impairment to this mix.

5. Market Microstructure of LRTs: Discounts, Premia, Liquidity

LRTs trade in an environment shaped by redemption logic, pool design, leverage, and shifting narratives.

5.1 Price vs. Underlying Value

An LRT’s theoretical value is the NAV of its backing:

• ETH or LST principal.
• Accrued Ethereum staking rewards.
• Accrued AVS rewards (ETH, stablecoins, or AVS tokens).
• Minus slashing losses and protocol fees.

Market prices can diverge from NAV due to:

• Liquidity and slippage.
• Redemption delays and uncertainty.
• Required risk premia for slashing and governance risk.
• Speculation on future AVS rewards or airdrops.

LRTs tend to trade:

• At a discount when:

  • Slashing or AVS instability is feared.
  • Exit queues are long.
  • Leverage unwinds force selling into shallow markets.

• At a premium when:

  • Demand for restaking yield or airdrops is intense.
  • Deposits are capped.
  • Market participants heavily value future rewards.

5.2 On-Chain Liquidity and LP Concentration

LRT liquidity is usually concentrated in a handful of DEX pools (often LRT/ETH or LRT/stablecoin). Key features:

• Pool depth controls how much can be traded without big price moves.
• LP concentration determines resilience: if a few addresses supply most liquidity, their exits can dramatically drain depth.
• Incentives (liquidity mining, etc.) can temporarily deepen pools but may not be durable.

From LST markets such as rETH on Balancer, we see:

• Top three LPs frequently controlling 30–70% of liquidity, sometimes over 90%.
• Rapid shifts as large LPs rebalance.

Similar patterns are likely in LRT pools, especially in early growth phases.

5.3 CEX Listings

Some LSTs and potentially LRTs secure CEX listings:

• This adds liquidity venues and broadens the user base.
• It enables cross-venue arbitrage and more continuous price discovery.

It also introduces:

• Custodial and venue risk.
• The possibility of sudden halts or delistings that disrupt arbitrage and deepen on-chain dislocations.

For many LRTs, especially newer ones, liquidity remains primarily on-chain.

5.4 Leverage and Yield Farming Structure

LRTs are deeply embedded in DeFi strategies:

• As collateral in lending markets.
• In yield-bearing structured products (e.g., Pendle).
• In combinations with options and perps to lever restaking exposure.

This structure creates:

• Reflexivity: high yields attract capital, raising TVL and narrative momentum, which attracts more integrations and liquidity, which further strengthens the story.
• Hidden leverage: stacking leverage (borrow against LRT to buy more LRT, then farm with it, etc.) can create very large effective exposure vs. underlying collateral.

In bull phases, this supports tight spreads and strong liquidity. In stress, it reverses quickly: LPs pull liquidity, leverage unwinds, and slippage spikes.

6. Competitive Landscape: EigenLayer, Symbiotic, Karak

Restaking has become a multi-protocol arena, not just an EigenLayer story.

6.1 EigenLayer: ETH-Focused Shared Security

EigenLayer is the flagship:

• Primarily focused on Ethereum and ETH-denominated security.
• Deep integration with LSTs (stETH and others) and LRTs (Ether.fi, Renzo, Kelp DAO, etc.).
• Very high TVL, peaking around 19.5 billion USD in 2024 and moderating to ~14.5 billion later.

Strengths:

• First-mover advantage and strong brand.
• A broad AVS ecosystem and active experimentation.
• A clear mental model for pooled security and AVS marketplaces.

Risks:

• Acts as a systemic hub due to high concentration.
• Complex governance around intersubjective slashing.
• Heavy dependence on ETH and ETH-derivatives ties its fate to Ethereum’s own health.

6.2 Symbiotic: Alternative Design

Symbiotic launched in early 2025 and quickly drew around 1.5 billion USD in TVL:

• The core idea matches EigenLayer’s: multiple protocols share a security pool.
• Collateral and slashing/governance details differ.

Potential differentiators:

• Different collateral flexibility and AVS integration.
• Alternate trade-offs in slashing (more or less objective vs. intersubjective).
• Varying incentive models for operators and restakers.

Risks:

• Less battle-tested.
• New codepaths and governance surfaces.
• Further fragmentation of restaked security.

6.3 Karak: Multi-Asset Restaking

Karak focuses on multi-asset restaking, with around 2–3% market share:

• Supports collateral beyond ETH, including other PoS tokens or stablecoins.
• Extends the restaking concept beyond Ethereum.

Advantages:

• Collateral diversification could reduce pure ETH exposure.
• Useful for protocols wanting non-Ethereum security pools.

Challenges:

• Each asset type brings different slashing regimes, liquidity, and regulatory profiles.
• Cross-chain and cross-asset complexity adds systemic risk if mishandled.

6.4 Quick Comparison

A simplified snapshot:

The likely end state is a multi-protocol restaking ecosystem with cross-platform interactions and security arbitrage. That both diversifies and complicates risk.

7. Risk Shifts for Validators, LST Holders, and LRT Holders

Restaking changes the risk/return setup for three key groups.

7.1 Validators and Operators

For validators/operators, restaking adds:

• New revenue: AVS rewards and incentives.
• Higher complexity: multiple AVSs, more software, more monitoring.
• Broader slashing surface: each AVS adds new ways to fail.

Risk shifts:

• From a single, well-understood consensus risk to a basket of AVS-specific, governance, and implementation risks.
• From purely objective enforcement to a mix of objective and intersubjective slashing.

Operators must choose:

• How many AVSs to support and with what allocations.
• How to architect infra to avoid correlated outages.
• How to price their services to reflect added tail risk.

7.2 LST Holders

For LST holders, restaking brings:

• Optionality: ability to restake directly or via LRTs to earn more yield.
• Additional exposures:
  • Restaked LSTs face AVS slashing on top of Ethereum’s base-layer risk.
  • LSTs posted into LRT protocols also pick up protocol and AVS risk.

Even LST holders who don’t restake are affected indirectly:

• If much of an LST’s supply is restaked and a major slashing event hits, the LST’s market price may drop due to association, liquidity stress, and changed risk perception.
• Lending protocols may adjust parameters (collateral factors, caps) as restaking risk evolves, affecting LST utility.

7.3 LRT Holders

LRT holders carry the most layered exposure:

• Base-layer risk: Ethereum consensus and staking.
• LST risk: if the backing is LST-based, including depegs and liquidity/queue risk.
• Restaking/AVS risk: AVS faults, governance decisions, operator incidents.
• Protocol risk: LRT smart contracts and governance.
• DeFi risk: integration into lending and structured products, with liquidation cascades.

Owning LRTs is effectively underwriting:

• Ethereum security and value,
• AVS performance and governance,
• LRT protocol risk management,
• DeFi market stability.

Yield compensates for this stack, but the tail can be brutal.

8. Scenario Analysis: Bull, Base, Bear

Given the system’s complexity, scenarios are more useful than point predictions.

8.1 Bull Scenario: Restaking Becomes Core Infrastructure

In the upside path:

• AVSs harden
  Code is well-audited; cryptoeconomic designs are robust. Objective slashing dominates; intersubjective cases are rare and transparent.

• Operators professionalize
  They invest heavily in infra and risk controls. Correlated failures across AVSs are rare, and prudent stake allocation caps damage.

• Stable LST/LRT markets
  Liquidity is deep across DEXs and CEXs. LP concentration declines as more participants provide liquidity. Exit queues are usually short.

• Prudent DeFi integration
  Lending and structured products use conservative LTVs, dynamic collateral factors, and circuit breakers for LSTs/LRTs. Leveraged restaking exists but is bounded.

• Real economic usage
  AVSs deliver valuable services (DA, sequencing, oracles, cross-chain security) with sustainable fee flows. Restaking yield leans more on fees and less on one-off incentives.

• Healthy competition
  EigenLayer, Symbiotic, Karak, and others coexist. Restakers diversify across platforms. Competition pushes better security and governance.

Restaking settles in as core infrastructure. LSTs and LRTs are treated as credible, yield-bearing assets with understood, managed risk.

8.2 Base Scenario: Growth with Periodic Shocks

In a middle path:

• Continued adoption
  TVL grows but at a steadier pace after the initial wave. AVSs multiply, but only a subset reaches durable, large-scale usage.

• Periodic incidents
  AVS bugs, operator failures, and governance disputes cause localized slashing and occasional LRT depegs. These events hurt but are ultimately contained.

• Ongoing repricing
  Markets gradually learn to charge for restaking risk. LRTs trade at structural discounts to NAV, especially for AVS-heavy portfolios.

• Rising regulatory focus
  Authorities scrutinize liquid staking and restaking, particularly where leverage and retail users mix. Some jurisdictions add constraints around marketing and usage.

• Fragmented ecosystem
  Multiple restaking platforms thrive, but EigenLayer remains dominant. Cross-platform security and composability develop slowly, leaving some inefficiencies.

Restaking is important but visibly risky. Sophisticated players treat LRTs as high-yield, high-risk instruments, not stable cash-like assets. DeFi protocols incrementally harden around these realities.

8.3 Bear Scenario: Systemic Slashing and Contagion

In the severe downside:

• A major AVS blows up
  A widely used AVS (e.g., a shared sequencer or DA layer) is exploited or fails economically. Large-scale slashing of restaked ETH follows.

• Operator correlation bites
  Multiple top operators are hit at once, either because they all run the AVS or share a vulnerability. Several AVSs trigger slashing in quick succession.

• LRT markets unravel
  LRTs heavily exposed to affected AVSs see their collateral impaired. Prices crash relative to ETH, reflecting both realized losses and fear of more.

• DeFi cascades
  LRTs and LSTs used as collateral get marked down. Underwater loans trigger mass liquidations. Liquidations dump into thin markets, worsening depegs and forcing further liquidations.

• Exit gridlock
  Restakers rush to exit EigenLayer and unstake. Exit queues balloon, making redemptions slow and uncertain. Primary exits clog, pushing more holders to sell at steep discounts.

• Confidence collapse
  Restaking’s reputation takes a major hit. AVSs struggle to attract security. LRT TVLs shrink. Some LSTs are dragged down via association and integration.

• Broader spillover
  If restaking had grown to a large share of Ethereum staking and DeFi collateral, this event bleeds into ETH price, DeFi TVL, and cross-chain markets.

The response is likely:

• Sharp cuts in restaking participation.
• Aggressive risk controls and governance overhauls.
• Heightened regulatory response focused on leveraged restaking and derivative staking products.

9. Risk and Scenario Summary

A concise comparison across scenarios:

10. Conclusion

Restaking and shared security reshape how cryptoeconomic security is provisioned and traded. Letting the same staked capital secure multiple protocols boosts capital efficiency and unlocks a broad AVS ecosystem without each project having to build its own validator set.

The tradeoff is a new class of correlated, often opaque risks:

• Slashing extends beyond base-layer consensus into diverse AVSs, some with subjective fault regimes and unproven designs.
• Validators and operators juggle multiple services and correlated operational risks.
• LSTs and LRTs sit at the junction of staking, restaking, and leveraged DeFi, where minor price moves can trigger liquidation spirals.
• LRT liquidity is still maturing, with concentrated LP bases and incentive-driven depth.

EigenLayer’s rapid growth, the rise of LRTs from Ether.fi, Renzo, Kelp DAO, and others, and the emergence of Symbiotic and Karak show that markets are willing to take these risks in pursuit of yield and flexible security provisioning.

Where this lands depends on:

• How robust AVS code and slashing designs become.
• How professional and diversified operators are.
• How deep and distributed LST/LRT liquidity gets.
• How cautiously DeFi integrates these assets and manages leverage.
• How governance and regulation evolve around shared security.

Either way, restaking turns staking from a straightforward yield strategy into a complex web of interlinked exposures. Anyone considering LST or LRT exposure needs to understand slashing rules, shared security mechanics, and the microstructure of these markets-not just headline yields.